The core of the innovation is a circuit architecture known as a parity–time-symmetric, or PT-symmetric, system. The concept entered the wireless power arena in 2017, when researchers showed that a nonlinear PT-symmetric circuit could maintain robust power transfer across changing distances without any active tuning. In such a system, the transmitter and receiver are coupled resonators whose gain and loss are carefully balanced. When the coupling between them changes—because the distance or orientation changes—the coupled system automatically adjusts its oscillation frequency to remain in the symmetric regime, and the transfer efficiency stays remarkably flat. In other words, the physics of the circuit does the compensation that engineers would otherwise have to perform with sensors, feedback loops, and adjustable matching networks. The new work extends this principle from rigid laboratory coils to the far messier environment of living tissue and deformable electronics.

The system consists of two halves. On the outside is a self-oscillating wearable transmitter, a compact unit that generates an oscillating magnetic field without needing an external drive signal locked to a fixed frequency. Because the transmitter is self-oscillating, it naturally follows the resonant state of the coupled system as conditions change. On the inside is a stretchable receiver built from liquid metal, a material choice that lets the implanted coil deform freely with the surrounding tissue without cracking or losing its electrical properties. Conventional receivers made from solid copper traces on flexible substrates can survive some bending, but repeated strain at the millimeter scale of a moving organ eventually fatigues the conductors. Liquid-metal conductors, by contrast, remain electrically continuous even when stretched, twisted, and compressed, which is precisely what an implant attached to cardiac tissue experiences continuously.

The significance of combining these elements is best appreciated by considering what happens in a traditional inductive link. The efficiency of near-field coupling falls off steeply as the separation between coils grows or as they drift out of alignment, and deformation of the receiver changes its inductance and shifts its resonant frequency. For a soft implant, all three perturbations occur simultaneously and unpredictably. Earlier battery-free implants, including the fully implantable and bioresorbable cardiac pacemakers demonstrated in 2021, relied on near-field inductive coupling that worked well under controlled conditions but demanded careful positioning of the external power source. The PT-symmetric approach reported now absorbs those variations into the circuit dynamics, so the power delivered to the implant remains stable even as the geometry of the link changes throughout the day and with every heartbeat.

To validate the design, the researchers moved beyond benchtop tests with artificial phantoms and into large-animal models. In experiments with rabbits and pigs, the wearable transmitter powered a soft implantable device well enough to pace the heart without wires or batteries. Cardiac pacing is an unusually demanding test case: the implant must deliver precisely timed electrical stimuli to excite heart muscle, the power budget is unforgiving, and the mechanical environment is among the most dynamic in the body. The fact that the link tolerated the combined effects of respiration, motion, and tissue deformation while still delivering sufficient energy for reliable pacing suggests that the variation-tolerance is not a laboratory curiosity but a property that holds up under physiologically realistic conditions.

The study also sits within a broader materials-science context that has matured rapidly in recent years. Reviews of soft bioelectronics have catalogued the design and integration strategies needed to build devices that conform to biological tissue while maintaining long-term function, and complementary work on stretchable hermetic seals has addressed one of the field’s chronic weaknesses: keeping bodily fluids from permeating into stretchable electronics over time. A liquid-metal receiver must be encapsulated so that neither the metal nor the surrounding fluids leak across the interface, and advances in viscoplastic sealing strategies have made such packaging feasible without sacrificing mechanical compliance. The power system described in the new paper is thus the product of converging progress in circuit theory, soft materials, and encapsulation technology rather than a single isolated breakthrough.

From a clinical standpoint, the implications extend well beyond pacing. Battery-free bioelectronics have been proposed for nerve stimulation, wound monitoring, drug delivery, and closed-loop therapies in which a sensor and a stimulator work together. Every one of those applications inherits the same coupling problem: the implant moves, deforms, and drifts relative to whatever external source powers it. A power link that is insensitive to distance, alignment, and strain removes a fundamental constraint on where such devices can be placed and how they can be designed. An implant no longer needs to be anchored in a fixed orientation relative to a wearable patch, which simplifies surgical placement, improves patient comfort, and opens the door to implants on organs that are in near-constant motion.

There are, of course, questions that must be answered before such systems reach routine clinical use. The reported demonstrations were in animal models, and translating to humans will require attention to the long-term biocompatibility of the liquid-metal receiver and its packaging, the thermal footprint of continuous power delivery through tissue, and the regulatory pathway for a wearable component that patients must wear consistently. The efficiency of any wireless link also depends on the power levels involved; pacing requires relatively modest energy, and applications with higher demands may stress the PT-symmetric architecture differently. The nonlinear dynamics that make the circuit robust also impose constraints on how much power can be transferred before the system exits its stable operating regime, a trade-off that future engineering will need to map carefully.

Even with those caveats, the demonstration marks a meaningful shift in how the field thinks about wireless power for soft implants. Rather than treating movement and deformation as disturbances to be minimized, the design embraces them as conditions the circuit is built to withstand. That philosophical change—designing for variation rather than against it—may prove as important as any single performance metric. If the approach scales as its animal results suggest, the combination of a self-oscillating wearable transmitter and a stretchable liquid-metal receiver could become a standard platform for powering the next generation of untethered, battery-free medical implants, bringing the vision of soft electronics that live comfortably inside the moving, changing human body considerably closer to reality.

Subject of Research: Variation-tolerant wearable-to-implant wireless power transfer for soft, battery-free medical implants

Article Title: Wireless power transfer that tolerates movement and deformation for soft implants

Article References: Wireless power transfer that tolerates movement and deformation for soft implants. (2026). Nature Electronics. https://doi.org/10.1038/s41928-026-01715-z

Image Credits: AI Generated

DOI: 10.1038/s41928-026-01715-z

Keywords: wireless power transfer, parity-time symmetry, soft implants, liquid metal, cardiac pacing, wearable electronics, bioelectronics, stretchable electronics, battery-free implants, Nature Electronics, biomedical engineering, resonant coupling

The core difficulty is familiar to anyone who has wrestled with wireless phone chargers. Conventional inductive coupling depends on two coils being tuned to the same resonant frequency, like two tuning forks sharing a pitch. When the coils are perfectly aligned and the load is steady, energy flows efficiently across the gap. But inside a body, nothing stays still. A patient moves, breathes, and shifts posture; the implant stretches with surrounding tissue; the electrical impedance at the implant’s output changes as it stimulates muscle. Each of these disturbances shifts the resonance of the system, and once transmitter and receiver drift out of tune, power transfer efficiency collapses. The result is an implant that works beautifully on the bench but fails unpredictably in the clinic, a problem engineers have documented since early analyses of radio-frequency coils in implantable devices in the 1980s.

The Korean team’s solution attacks the problem on two fronts simultaneously: the circuit architecture and the materials. On the circuit side, they built the power link around a nonlinear parity-time symmetric circuit, an approach first demonstrated for robust wireless power transfer by researchers at Stanford University in 2017. In a parity-time symmetric system, the transmitter and receiver form a symmetric resonator pair, and the transmitter continuously adjusts its own operating frequency to match whatever frequency the receiver settles at. Instead of demanding that the two coils stay perfectly tuned, the system lets the receiver’s resonance wander and follows it in real time. A feedback circuit in the wearable transmitter detects changes and provides automatic frequency adaptation, so that misalignment, mechanical deformation, or load impedance fluctuations no longer break the resonance that carries the power.

On the materials side, the implantable receiver is built from liquid metal. Rather than patterning the receiver’s coil and interconnects from rigid copper, the researchers used liquid metal conductors encapsulated in soft elastomers, fabricated through a multistep process involving photolithographically defined copper traces that guide liquid metal deposition, vertical interconnect vias etched through insulating elastomer layers, and off-the-shelf electronic components integrated onto liquid metal pads. Because the conductors are intrinsically stretchy, the receiver’s electrical resistance barely changes when the device deforms. That matters because resistance-induced losses are what erode efficiency in stretchable electronics: a conventional serpentine metal trace stretches by uncoiling and thinning, raising resistance and shifting the coil’s electrical properties. A liquid metal channel simply changes shape while keeping its cross-section and conductivity largely intact, minimizing resistance-induced power losses during deformation.

The performance numbers reported in the paper are striking. The system maintains power transfer efficiency above 50 percent even when the receiver is stretched by 30 percent or displaced laterally by 30 millimeters from the transmitter. For context, conventional inductively coupled systems can lose the bulk of their efficiency at a fraction of that misalignment. The team also tested the system under bending, tilting, and rotational misalignment, tracking both operating frequency and transfer efficiency through each disturbance, and compared the parity-time symmetric architecture against a conventional negative-impedance-converted system in simulations and in live animals. In those comparisons, the conventional system’s efficiency maps showed sharp drop-offs as separation and strain increased, while the parity-time symmetric system held a broad, stable operating range. A radar chart comparison against previously reported stretchable wireless bioelectronic devices, scored on efficiency, alignment tolerance, strain insensitivity, stretchability, and conductivity, placed the new system at the most balanced and superior overall performance among state-of-the-art devices.

The demonstration that turns this from an elegant circuit exercise into a potential medical technology is cardiac pacing. The researchers packaged the liquid metal receiver as a wireless pacemaker with electrodes made from a silver-gold nanowire composite embedded in an elastomer, designed to be sutured onto the surface of the heart. The wearable transmitter, powered by an 11-volt lithium-ion battery and managed by a microcontroller-controlled power unit, was mounted on the outside of the body. The received power is rectified on the implant and used to deliver controlled electrical stimulation to the cardiac tissue. Because the entire power link tolerates the constant motion of a large animal, the pacing could continue under highly dynamic in vivo conditions that would destabilize a conventional link.

In experiments, the team first validated the system in rabbit models, implanting the liquid metal receiver subcutaneously and using an LED indicator on the receiver to visualize successful power transfer as the axial and lateral separation between transmitter and receiver coils increased. The parity-time symmetric system maintained consistent activation across the tested positions, whereas the conventional system failed to power the implant at increased distances and lateral misalignments. The team then moved to a porcine model, whose heart size and physiology are much closer to humans. Fully untethered pigs with wearable transmitters attached to their backs received wireless epicardial pacing, with electrodes sutured onto the heart surface. The system not only paced the heart reliably but also terminated tachyarrhythmias, dangerously fast heart rhythms, under those same dynamic conditions, demonstrating that the power link could support not just routine pacing but active intervention during cardiac events.

The control side of the system reflects a deliberate design for real-world use. The wearable power management unit regulates power from the battery and provides regulated outputs to the transmitter circuits through a microcontroller-controlled switch, and the microcontroller was programmed using the Arduino IDE, with source code available from the corresponding authors. Smartphone control was implemented using a commercially available Bluetooth Low Energy terminal application, meaning a clinician or patient could in principle adjust the system with ordinary consumer hardware rather than bespoke equipment. That kind of practical engineering detail, mundane as it sounds, often separates laboratory demonstrations from technologies that can actually be deployed and maintained outside a research environment.

The implications extend well beyond pacemakers. Fully implantable bioelectronic systems, from neuromodulation devices and brain-computer interfaces to bioresorbable stimulators and injectable sensors, all face the same power bottleneck, and many of the most exciting recent devices in the field, including millimetre-scale bioresorbable optoelectronic systems and programmable ultrasonic implants, depend on some form of wireless energy delivery. A power link that tolerates strain, misalignment, and load variation could serve as a general-purpose energy backbone for soft, body-conformal devices that move with organs rather than fighting them. The authors’ comparison framework, scoring devices on efficiency, alignment tolerance, strain insensitivity, stretchability, and conductivity, also gives the field a clearer yardstick for evaluating future designs.

Cautious optimism is warranted. The work was demonstrated in animal models, and the path to human devices will require the usual gauntlet of biocompatibility validation, long-term reliability testing, and regulatory review; the liquid metal, elastomers, and nanocomposite electrodes involved have strong precedents in the soft bioelectronics literature but must prove themselves in chronic implants. Still, the combination of a self-tuning nonlinear circuit and an intrinsically stretchable liquid metal receiver addresses the two failure modes, resonance drift and deformation losses, that have most reliably broken wireless implants. If the variation tolerance demonstrated in freely moving pigs carries through to clinical devices, the era of truly untethered, maintenance-free implantable medicine moves considerably closer, powered by a transmitter you wear and a receiver that bends with every beat of your heart.

Subject of Research: Variation-tolerant wearable-to-implant wireless power transfer for implantable bioelectronics such as wireless cardiac pacemakers

Article Title: A wearable-to-implant wireless power transfer technology with variation tolerance

Article References: Nam, S., Seo, T., Yoo, S., Park, C., Kim, T., Yu, M., Kim, Y., Kang, H., Yeom, D., Cho, Y., Cho, H., Lee, D., Kim, J. H., Sunwoo, S.-H., Lee, S., Moon, J., Lee, S.-P., Kim, S., & Kim, D.-H. (2026). A wearable-to-implant wireless power transfer technology with variation tolerance. Nature Electronics. https://doi.org/10.1038/s41928-026-01714-0

Image Credits: AI Generated

DOI: 10.1038/s41928-026-01714-0

Keywords: wireless power transfer, parity-time symmetry, liquid metal electronics, implantable bioelectronics, cardiac pacing, stretchable electronics, wearable devices, resonant inductive coupling, soft materials, biomedical engineering, Nature Electronics, tachyarrhythmia

The key to the new platform is the integration of three functions on a scale measured in tens of micrometres. Each robot carries a temperature sensor, a programmable CMOS control circuit and arrays of electrochemical actuators that beat like biological cilia. The actuators are built from a titanium–palladium stack that bends when voltage is applied, driving ions into and out of the palladium layer and causing controlled bending at engineered hinges. Because the cilia are hinged, with two rigid panels connected by rotational joints, their beat cycle can be programmed to break the time-reversal symmetry that governs fluid motion at low Reynolds number. At microscopic scales, where viscosity dominates over inertia, fluid flows are reversible unless the stroke and the recovery stroke differ in shape, a constraint famously articulated in Lighthill’s analysis of flagellar hydrodynamics. The hinged design allows the robots to sweep the fluid with a fast, extended stroke and a slow, folded recovery, producing net pumping in a chosen direction.

Powering and controlling such tiny machines is a formidable engineering challenge, and the team solved it with microscale photovoltaic regions that convert incident laser light at 635 nanometres into electrical current for both the logic and the actuators. The binary sensing circuit switches its output phase configuration at threshold temperatures of 26 and 30 degrees Celsius, and can deliver actuation frequencies ranging from 0.4 to 12.8 hertz, with 1.6 hertz used for cilium beating in the reported demonstrations. The photovoltaic supply generates currents of roughly 0.78 microamperes at one sun of illumination, rising to about 4.8 microamperes at ten suns, enough to run the circuit and drive the cilia without any tether. This architecture builds on earlier work from the same collaboration, including electronically integrated mass-manufactured microscopic robots, cilia metasurfaces for programmable microfluidic manipulation, and microscopic robots with onboard digital control, but it adds something those systems lacked: a genuine sensory loop in which the robot’s action depends on what it measures.

The researchers demonstrated three distinct temperature-responsive modalities, each producing a different coupling between robot behaviour and environmental cues. The first is binary sensing, in which the onboard sensor acts as a threshold detector. Below the threshold the cilia pump in one direction; above it, the circuit reconfigures the wiring to the actuators and the pumping reverses. The team showed that this simple switch can reverse unidirectional flow, reverse the rotation of a single vortex, or reverse both vortices in a symmetric counter-rotating pair, all in response to nothing more than the ambient temperature crossing a set point. Because the flow patterns are generated by arrays of individually wired cilia, the same sensing principle can be routed into dramatically different hydrodynamic outcomes simply by changing how the circuit output is distributed across the array.

The second modality replaces the sharp threshold with continuous sensing, implemented using pulse-coupled oscillator circuits built from dynamic-leakage-suppression logic gates. In this scheme, the oscillation frequency of the onboard circuit depends on temperature through the exponential dependence of subthreshold leakage currents, so the beat frequency of the cilia rises smoothly as the fluid warms. The pumping speed therefore tracks temperature continuously rather than switching between two states. The oscillator architecture also supports synchronisation: coupling pulses from a designated leader oscillator advance the follower until the two lock with a fixed phase offset, a mechanism related to the pulse-coupled designs previously used for coordinating autonomous microscopic machines through local electronic pulses. This phase-locking provides a route to coordinated actuation across a cilia array without any central controller.

The third modality exploits spatial temperature gradients rather than absolute temperature. Using a scalable ultra-low-power temperature gradient sensor based on pulse-coupled oscillators, the robot determines which side of its body is warmer and aligns its pumping accordingly. When the imposed gradient is reversed, the leader and follower roles in the oscillator network exchange, and the pumping direction flips. The result is a microscopic machine that orients its fluid-mechanical output along the local thermal landscape, in loose analogy to the way ciliated protists orient their swimming relative to environmental cues. Together, the three modalities, threshold switching, continuous modulation and gradient alignment, form a toolkit for programming how a microrobot’s behaviour responds to its surroundings.

The most striking demonstration is the closing of the loop. Because the cilia-driven flows move fluid around, they transport heat, and the team showed that a collective array of cilia can actively reshape the local thermal field. In their experiments, unidirectional pumping advected fluid from a cooler region towards a hotter region, flattening the temperature gradient that the sensors were measuring. As the temperature field changed, the oscillator frequencies shifted, which in turn altered the pumping, which further modified the thermal field. The researchers modelled this feedback with a reduced two-dimensional description of thermally advected flow and quantified it through a thermal stretching length that grows cycle by cycle, fitting the dynamics with an exponential law. The system thus exhibits a genuine closed-loop interaction among sensing, actuation and environment, the microscopic analogue of an organism modifying its own habitat.

The experimental platform itself is a tour de force of integration. The devices were fabricated at the Cornell NanoScale Facility, with the CMOS circuits exposed by etching the top dielectric, then interconnected with titanium–platinum leads, re-encapsulated in silicon dioxide, shielded with a grounded layer, and finally released with an aluminium nitride sacrificial layer before the titanium–palladium actuator stack and rigid panels were added. Fluid temperatures were controlled with a hotplate and characterised by infrared thermography on dry samples, which showed a uniform region with a spatial standard deviation of about 0.11 degrees Celsius and a gradient region of roughly 0.5 degrees Celsius per millimetre; in liquid, where phosphate-buffered saline strongly absorbs mid-infrared radiation, the team used a micro-thermocouple instead. Flow fields were measured with particle image velocimetry and compared against three-dimensional hydrodynamic simulations of the beating cilia, validating the theoretical model of the two-hinged kinematics.

The implications reach well beyond the laboratory demonstration. Artificial cilia that sense and respond to their environment could regulate temperature and chemical gradients in lab-on-chip systems, create fluid microhabitats for recruiting cells or controlling microbiomes in biomedical contexts, and serve as building blocks for emergent collective behaviours in swarms of autonomous micromachines. The theoretical framing, developed with physicists studying non-reciprocal phase transitions and adaptive active solids, suggests that populations of such robots could self-organise into patterns no single robot is programmed to produce, much as bacterial colonies generate large-scale spiral waves from local interactions. Because the platform is built with standard CMOS processes and mass-manufacturable techniques, scaling to larger and more capable microrobotic collectives appears feasible. The work was supported primarily by the National Science Foundation and the Army Research Office, with additional support from the Kavli Institute at Cornell and Westlake University, and the team has filed patent applications covering the actuators and control electronics.

What makes this advance conceptually important is the shift from open-loop microrobots, which execute fixed motions, to machines that participate in a dynamic dialogue with their world. In nature, the coupling between organism behaviour and environmental modification underlies nest construction, swarm thermoregulation and microbiome recruitment; until now, engineered microrobots could sense or act, but rarely both in a feedback loop. By embedding thermal sensing, programmable logic and ciliary actuation on a single chiplet smaller than a grain of salt, the Cornell-led team has created the first microscopic robotic platform in which the environment is not merely a medium the robot moves through but a variable the robot actively regulates. As the researchers and their collaborators refine the sensing modalities and extend them to chemical and mechanical cues, the boundary between living, environment-shaping matter and engineered machines at the microscale looks set to blur further.

Subject of Research: Microscopic robots with onboard sensing, logic and cilia actuators that couple their behaviour to thermal environmental cues in a closed loop

Article Title: Microscopic robots that sense and reshape their environment

Article References: Wang, W., Zhang, J., Chaudhari, P., Shim, K., Severn, J., Zheng, C., Liang, Z., Ji, Y., Pelster, J., Griniasty, I., Seara, D., Vitelli, V., Lauga, E., Apsel, A., & Cohen, I. (2026). Microscopic robots that sense and reshape their environment. Nature Electronics. https://doi.org/10.1038/s41928-026-01709-x

Image Credits: AI Generated

DOI: 10.1038/s41928-026-01709-x

Keywords: microrobotics, artificial cilia, CMOS, electrochemical actuators, temperature sensing, microfluidics, closed-loop control, NEMS, collective behaviour, thermal regulation, Cornell University, Nature Electronics

The concept of engineered cilia draws its inspiration directly from biology. Cilia are the tiny, hair-like appendages that line surfaces throughout living organisms, from the airways of the human lung, where they sweep mucus and trapped debris along in coordinated waves, to the surfaces of single-celled organisms that use them to swim and to feed. Biological cilia achieve their remarkable effectiveness through dense, coordinated arrays in which individual filaments beat in synchrony, generating directed fluid flow with exquisite efficiency. Replicating that behavior in artificial microsystems has been a long-standing goal of microfluidics and microrobotics, because arrays of artificial cilia could, in principle, replace bulky external pumps and valves with silent, solid-state surfaces that move fluids on demand.

What sets the new work apart is the marriage of actuation with sensing and with mainstream semiconductor manufacturing. CMOS technology is the workhorse of the modern electronics industry, responsible for the billions of transistors that power everything from smartphones to data centers. By integrating microrobotic cilia onto CMOS platforms, the researchers inherit the maturity, scalability and precision of chip fabrication. Each cilium can be addressed and driven by underlying circuitry, and the same silicon infrastructure that powers the actuators can also host the sensors that monitor local conditions. In the demonstration discussed by Hu, the relevant environmental variable is temperature: the cilia respond to thermal cues by adjusting their beating, and in doing so they pump fluid across the chip surface.

The technical significance of this sensing-actuation coupling is difficult to overstate. Most microscale actuators to date have been open-loop devices: they perform a prescribed motion when stimulated, blind to the consequences of that motion. A cilium that can detect temperature and then modify fluid flow creates a feedback system embedded in the material itself. Temperature affects fluid viscosity, density gradients and chemical reaction rates, so a surface that senses heat and stirs fluid in response can, for example, redistribute thermal energy, homogenize concentration gradients or deliver reagents to where they are needed. The microrobotic cilia thus function simultaneously as sensors, actuators and pumps — three components that traditionally occupy separate devices and separate design disciplines.

This achievement builds on a decade of steady progress in microrobotics. Earlier landmark work by Marc Miskin and colleagues, published in Nature in 2020, introduced microscopic robots small enough to be invisible to the naked eye, capable of crawling under external stimulation and fabricated using processes compatible with existing semiconductor foundries. Subsequent work by the same community, including studies published in Proceedings of the National Academy of Sciences in 2018 and by Wang and colleagues in Nature in 2022, pushed toward ever more capable and controllable microrobotic systems. The new CMOS-integrated cilia represent a conceptual step beyond locomotion: rather than robots that move themselves through an environment, these are robots that stay put and transform the environment around them, one fluid pulse at a time.

Coordination is the second pillar of the achievement. A single cilium, whether biological or artificial, moves very little fluid. The power of ciliary systems emerges from collective behavior — thousands or millions of filaments beating in metachronal waves, the traveling patterns of motion that make biological cilia so effective. The theoretical foundations for such collective synchrony were laid long ago: the Kuramoto model, formalized by Mirollo and Strogatz in 1990, describes how large populations of coupled oscillators spontaneously fall into step, and the elegant geometry of ciliary coordination was analyzed by King, Ocko and Mahadevan in 2015. Nature offers a striking biological parallel in the aggregation patterns of bacteria such as E. coli, documented by Budrene and Berg in 1991, where individual cells following simple rules produce elaborate collective structures. The new microrobotic cilia tap into this same physics, using engineered coupling — mediated in part by the fluid they share and in part by their CMOS control layer — to generate coordinated pumping from individually simple units.

The fluid itself plays a crucial role in this coordination. At the microscale, fluid dynamics is dominated by viscosity rather than inertia, a regime characterized by low Reynolds numbers where momentum essentially does not exist and motion is entirely determined by the forces applied at each instant. Hydrodynamic interactions between neighboring cilia are strong and long-ranged in this regime, so the beating of one filament physically influences its neighbors through the surrounding fluid. This creates natural pathways for synchronization and wave propagation, and it means that the cilia and the fluid form a single coupled dynamical system. When the cilia sense a temperature change and alter their beating, they are not merely responding to their environment — they are renegotiating their collective behavior with it, moment by moment.

Potential applications span several fields. In microfluidics, CMOS-integrated ciliary surfaces could replace external pumps in lab-on-a-chip diagnostic devices, enabling fully portable analysis systems in which fluid handling is performed by the chip surface itself. In thermal management, the ability to sense hot spots and direct cooling flow toward them could transform how heat is removed from densely packed electronics, from high-performance processors to power electronics. In biology and medicine, surfaces that sense local conditions and stir fluid accordingly could improve cell culture systems, tissue engineering scaffolds and implantable devices, where gentle, distributed fluid motion is often essential for nutrient delivery and waste removal. Because the technology is CMOS-compatible, it could in principle be scaled to large areas using existing foundry infrastructure, a decisive advantage over exotic microfabrication approaches that never leave the laboratory.

The work also signals a broader philosophical shift in how researchers think about microscale machines. The traditional paradigm treats sensing and actuation as separate subsystems, connected by a controller — an architecture inherited from macroscopic robotics. At the microscale, where power, space and computational resources are all severely constrained, that separation becomes a liability. Systems in which sensing, computation and actuation are fused into a single integrated platform, as the CMOS cilia demonstrate, point toward microrobots that behave less like programmed machines and more like adaptive materials — surfaces whose mechanical response is inseparable from their perception of the world. Hu’s commentary frames this as a demonstration of how microscale actuators can both sense and actively modify their surroundings, a formulation that captures the essence of embodied intelligence at the smallest scales.

Challenges remain before such systems become commonplace. Driving dense arrays of actuators requires careful power management on-chip; long-term reliability of moving micromechanical structures in fluid environments must be established; and the repertoire of sensed variables will need to expand beyond temperature to include chemical composition, pressure and biological signals if the full vision of environment-shaping microrobotic surfaces is to be realized. Yet the trajectory is clear. From the first demonstrations of microscopic robots to today’s CMOS-integrated cilia that feel heat and answer with fluid motion, the field is converging on machines that live in, understand and reshape their microscopic worlds. As the boundaries between sensors, actuators and electronics continue to dissolve, the humble cilium — nature’s oldest micromachine — may prove to be the blueprint for the next generation of intelligent surfaces.

Subject of Research: CMOS-integrated microrobotic cilia that sense temperature and pump fluid to modify their microscale environment

Article Title: Microrobotic cilia that sense and reshape their surroundings

Article References: Hu, W. (2026). Microrobotic cilia that sense and reshape their surroundings. Nature Electronics. https://doi.org/10.1038/s41928-026-01716-y

Image Credits: AI Generated

DOI: 10.1038/s41928-026-01716-y

Keywords: microrobotics, CMOS integration, artificial cilia, microfluidics, temperature sensing, actuators, fluid pumping, collective synchronization, lab-on-a-chip, thermal management, Nature Electronics, microscale robotics

The central problem the team set out to solve is one that has dogged electronic textiles for years: how to embed genuine circuit-level functionality into fibres without wasting the fibre itself or creating fragile connections. Previous strategies often attached discrete components onto the surface of a fibre or built devices that used only a small fraction of the fibre volume. Interconnects between components tend to be unstable under the repeated bending, stretching and abrasion that clothing endures. Poor fibre utilization means much of the material contributes nothing to the electronics, while unstable interconnects mean the circuits fail exactly when a garment is treated like a garment.

The Donghua group’s answer is to make the fibre itself the circuit. Their microfluidic spinning process flows different functional materials through the channels of a spinning device, combining conductive layers with layers that perform specific functions. By switching the flow rates of the incoming streams during spinning, the researchers can arrange distinct functional modules along the fibre axis in a programmable sequence, rather like writing code that the spinning apparatus executes in material form. The team supported this design step with fluid-encoding simulations, drawing on established understanding of how flow velocity governs behaviour in microfluidic channels, to control where each module forms within the continuously drawn fibre.

Crucially, the researchers designed their modules around four fundamental types of signal conversion. Electroluminescent modules handle electron-to-photon conversion, turning electrical drive signals into emitted light. Resistor and capacitor modules perform electron-to-electron conversion, providing the passive components needed for analogue signal processing such as filtering. Organic electrochemical transistor modules carry out electron-to-ion conversion, translating electronic currents into ionic motion within a semiconductor polymer and thereby enabling digital logic operations. Finally, electro-quasistatic modules provide electro-quasistatic modulation, which the team exploited for touchless sensing and control. Because all four module families can be produced and integrated within the same spinning process, a single fibre can combine them into a working circuit tailored to a specific task.

The demonstrations span an impressive functional range. Electroluminescent IC fibres emit light, and a single fibre can encode a variety of luminescent colours along its length, suggesting displays or lighting elements woven directly into fabric. Resistor-capacitor IC fibres perform analogue signal processing, implementing functions such as tuneable filtering. Fibres built around organic electrochemical transistors execute digital logic, including inverter circuits, bringing Boolean operations into the thread itself. The electro-quasistatic IC fibres are perhaps the most striking: they sense the approach of a body or hand without contact, exploiting the quasistatic electric field coupling between the fibre and nearby conductors, and can translate that sensing into control signals.

To show that this touchless capability is more than a laboratory curiosity, the researchers connected their electro-quasistatic fibres to real machines. In supplementary demonstrations, the fibres controlled a virtual drone, a physical drone and a robotic arm, and a robotic arm equipped with the fibres automatically tracked a target object. The team even calibrated the relationship between applied voltage and sensing distance using a simple cardboard sheet wrapped in aluminium foil, and tested how nearby conductors interfere with the fibres’ operation, work that speaks to the practical engineering needed before such sensors can live inside clothing in the messy electromagnetic environment of daily life.

Durability, the historic weak point of e-textiles, receives serious attention. The IC fibres can be woven, sewn and arranged into textiles and, according to the researchers, withstand repeated deformation and washing. This mechanical resilience builds on the group’s earlier work on stretchable conductive fibres, in which a worm-shaped graphene microlayer enabled ultrahigh tensile strain and stable conductance, and on their broader research into soft fibre electronics based on semiconducting polymers. By embedding the functional layers within the fibre structure rather than coating them on top, the spinning process protects the electronics from the mechanical abuse that destroys surface-mounted devices.

The study situates itself within a rapidly maturing field. Recent years have seen large-area display textiles, fabric-based optical communication using diode fibres, digital electronics in fibres enabling fabric-based machine-learning inference, a single-fibre computer capable of textile networks and distributed inference, and a chipless textile electronics platform based on body-coupled fibres. Related work has produced fibre-integrated circuits through multilayered spiral architectures and axially encoded metafibers created by sequence spinning. What distinguishes the new approach is the programmability of the spinning process itself: rather than assembling pre-made components, the researchers encode circuit topology into the fluid flows that form the fibre, so the circuit design and the fibre manufacturing become a single continuous operation.

The implications reach toward what the authors frame through the lens of embodied intelligence, the idea that sensing, processing and feedback should be integrated into a physical platform rather than separated across devices. A textile based on IC fibres could, in principle, sense its environment, process the signals locally and respond with light, logic or actuation, all within the fabric. The researchers also point to applications in robotics, where their fibres already steer machines, and in wearable platforms that merge biosensing, computation and feedback. The fact that the team has released source data through Figshare and code through Code Ocean underscores the engineering orientation of the work, inviting others to reproduce the fluid-encoding process and build on the module library.

Challenges remain before IC fibres appear in commercial garments. The reported work demonstrates individual functional fibre types and their integration, but scaling production, achieving the transistor densities of conventional silicon, and managing power delivery through textile interconnects are all open problems. The electro-quasistatic sensing mode, while elegant, will require careful engineering to remain reliable around the conductors and interference sources that surround any wearer. Yet the core achievement stands: a spinning process in which the circuit is programmed into the fibre as it forms, yielding threads that glow, compute, filter and sense, and that survive the washing machine. If the module library continues to grow, the line between textile and computer may soon be measured in micrometres rather than millimetres.

Subject of Research: Programmable microfluidic spinning of fibres containing integrated circuit modules for smart textiles

Article Title: Programmable spinning of integrated circuit fibres

Article References: Sun, F., Chen, W., Jiang, F., Wang, K., Liu, F., Hong, Y., Han, X., Zhao, M., Zhu, Y., Sun, H., Wang, H., Wang, G., & Zhu, M. (2026). Programmable spinning of integrated circuit fibres. Nature Electronics. https://doi.org/10.1038/s41928-026-01712-2

Image Credits: AI Generated

DOI: 10.1038/s41928-026-01712-2

Keywords: electronic textiles, microfluidic spinning, integrated circuit fibres, organic electrochemical transistors, electroluminescence, wearable electronics, soft materials, touchless sensing, semiconducting polymers, flexible electronics, smart fabrics, Nature Electronics

The central idea is deceptively simple. Instead of assembling electronic components onto a textile after the fact, or laminating rigid chips onto fabric surfaces, the new approach builds functionality directly into the fibre itself. A single fibre can be turned into an integrated circuit, meaning that the basic building blocks of electronics, such as the ability to switch, amplify or process signals, reside within a thread that can be woven, knitted or stitched using conventional textile processes. The commentary accompanies a research paper by Wang and colleagues published in Nature, which reports the underlying fibre technology.

Why does this matter? Wearable electronics has made impressive progress over the past decade, from smartwatches to electronic skin patches, but most devices still rely on rigid silicon hardware mounted in rigid or semi-rigid packages. That architecture clashes with the fundamental nature of fabric, which is soft, stretchable, porous and constantly deforming as the body moves. The mismatch between hard electronics and soft textiles has limited the comfort, durability and washability of smart garments, and it has constrained the density of functionality that can be packed into a piece of clothing. Fibre-level integration attacks the problem at its root by making the electronic function and the textile substrate one and the same.

The microfluidic encoding strategy at the heart of the work offers a route to that integration. By using fluid-handling techniques to pattern and define structures along the length of a fibre, the method effectively writes information, and therefore function, into the fibre. The result, as characterized in the Nature Electronics commentary, is that individual fibres become active circuit elements rather than mere conductive pathways. This distinction is crucial. Conductive threads have existed for years, serving as wires and electrodes in experimental garments, but wires alone do not make a circuit. Logic, memory and signal conditioning require components with nonlinear electrical behaviour, and embedding such behaviour into a fibre has been a persistent materials and manufacturing challenge.

Scalability is the second pillar of the advance. Laboratory demonstrations of fibre electronics have often depended on slow, bespoke fabrication methods that cannot realistically produce the kilometres of fibre needed for commercial textile production. The commentary emphasizes that the microfluidic encoding approach provides a scalable route, suggesting that the technique is compatible with continuous or high-throughput processing. If fibre-based circuits can be manufactured with the same industrial maturity as conventional yarns, the gap between laboratory prototypes and market-ready smart textiles narrows considerably.

The implications extend across multiple fields. In health monitoring, garments woven from circuit-bearing fibres could continuously capture physiological signals such as heart activity, muscle activity or temperature without the electrodes, straps and rigid modules that current wearables demand. The commentary’s own authorship reflects this orientation: Zhu, Chow and Yu are affiliated with the Department of Biomedical Engineering at City University of Hong Kong, the Hong Kong Centre for Cerebro-Cardiovascular Health Engineering, and the Institute of Digital Medicine, institutions focused on translating flexible electronics into biomedical applications. Textiles that sense and compute invisibly within clothing could enable long-term, unobtrusive monitoring of cardiovascular and neurological health, an area where intermittent clinical measurements often miss important dynamics.

Deformability is another theme the commentary stresses. Practical textile electronics must survive repeated bending, stretching, twisting and laundering while maintaining electrical performance. Because the circuit function is distributed along the fibre rather than concentrated in a rigid chip, the resulting devices can in principle deform with the fabric, distributing mechanical stress across the textile structure. This is what the authors mean by deformable textile electronics: electronics whose mechanical properties match those of the woven or knitted structures they inhabit. The approach also opens possibilities in human-machine interfaces, soft robotics, distributed sensing networks and energy-harvesting fabrics, where conformal, textile-native electronics could replace bulky conventional hardware.

The Nature Electronics commentary situates the work within a broader research landscape. Its reference list points to earlier milestones in the field, including studies of fibre and textile electronics published in Nature Electronics in 2024, a review of fibre-based electronic materials in Nature Reviews Materials in 2023, and work on deformable electronic materials in Nature Materials the same year. Together, these references trace the trajectory of the field: from material innovations that made fibres conductive and semiconducting, through device architectures that brought transistor-like behaviour to fibre formats, to the current push for fully integrated, manufacturable circuit fibres. The new Nature paper by Wang and colleagues, and the companion Nature Electronics article by Sun and colleagues cited in the commentary, represent the latest stage in that progression.

Challenges, of course, remain, and the commentary’s framing makes clear that turning circuit fibres into everyday products will require continued engineering. Interfacing fibre circuits with power sources and communication modules, ensuring long-term reliability through washing and wear, achieving electrical performance comparable to conventional silicon devices, and integrating fibre electronics into existing textile supply chains are all nontrivial tasks. Electrical and electronic engineering and neuroscience, the two subject classifications Nature assigns to the commentary, hint at the breadth of the application space, from signal processing hardware to the neural and physiological signals such hardware might one day record and interpret.

Even so, the vision articulated by Zhu, Chow and Yu is compelling. If the microfluidic encoding strategy fulfils its promise, the fibre, humanity’s oldest technological platform, could become its newest computational substrate. Clothing that senses, computes and communicates would no longer require attaching electronics to fabric but would instead emerge from the fabric itself, thread by thread, woven on the same looms that have clothed civilizations for millennia. That convergence of textile craft and microelectronics, the commentary suggests, may be the key that finally moves wearable electronics from the gadget era into the garment era.

Subject of Research: Microfluidic encoding of individual fibres into active circuit elements for deformable textile electronics

Article Title: Weaving circuits into fibres

Article References: Zhu, W., Chow, L., & Yu, X. (2026). Weaving circuits into fibres. Nature Electronics. https://doi.org/10.1038/s41928-026-01710-4

Image Credits: AI Generated

DOI: 10.1038/s41928-026-01710-4

Keywords: wearable electronics, fibre electronics, textile circuits, microfluidic encoding, deformable electronics, Nature Electronics, smart fabrics, biomedical engineering, flexible electronics, health monitoring, City University of Hong Kong, integrated circuit fibre

Spintronics, the field that exploits the spin of electrons rather than merely their charge, has long promised memory devices that combine non-volatility with high speed. In modern magnetic random-access memory, information is stored in the orientation of a nanoscale magnet and written by transferring angular momentum from a spin-polarized current, a mechanism known as spin–orbit torque. When a current flows through a heavy-metal layer adjacent to a ferromagnet, the resulting torque can switch the magnet’s direction, encoding a zero or a one. The efficiency of that process, however, is governed by the current pulses available from the surrounding circuitry, and this is precisely where the new work makes its mark.

Conventional electronic pulse generators struggle to deliver the combination of amplitude, duration and on-chip integration that aggressive magnetic switching demands. Fast transistors and modulated laser schemes have been explored, but each carries trade-offs in footprint, energy cost or compatibility with dense integrated circuits. The alternative demonstrated here is deceptively simple in concept: a nanoplasma switch embedded on the chip itself. When a modest voltage is applied across a nanoscale gap, the device undergoes a rapid ionization event, forming a miniature plasma discharge that conducts an intense burst of current for an extraordinarily brief interval before extinguishing itself.

The physics of these discharges is rooted in well-understood gas ionization and electron avalanche processes, yet the nanoscale confinement changes the picture considerably. Because the active region is so small, the plasma forms and collapses on timescales set not by bulky external electronics but by the intrinsic carrier dynamics within the gap. The result is a current pulse measured in picoseconds, far faster than the switching speeds of the transistors that would otherwise be needed. Importantly, the switch is not a laboratory curiosity built with exotic equipment; it is fabricated using processes compatible with standard chip manufacturing, which means pulse generation can sit directly alongside the magnetic elements it drives.

The central experimental finding reported in the underlying research is that pulses produced by these nanoplasma switches are fast enough and strong enough to control the magnetization of ferromagnetic heterostructures through spin–orbit torque. In such heterostructures — stacks in which a heavy metal with strong spin–orbit coupling is paired with a thin ferromagnetic layer — the direction of the applied torque depends on the direction of the current. A pulse that drives electrons one way can set the magnet to one orientation; a pulse in the opposite direction can set it to the other. Demonstrating reliable reversal under picosecond excitation is the critical milestone, because it shows that the write mechanism of spintronic memory can keep pace with the fastest pulse sources now available on chip.

Speed matters for more than bragging rights. As memory devices shrink, thermal fluctuations make their magnetic bits less stable, and the window of current amplitudes that switch the bit deterministically narrows. Ultrafast pulses change this balance: they deliver torque in a burst short enough that switching can complete before the accumulated heat spreads through the device, potentially improving energy efficiency while maintaining reliability. Prior studies have shown that sub-nanosecond currents can reduce switching energies relative to slower write operations, and the picosecond regime explored here pushes that trend toward its physical limits. The commentary also situates the result within a growing body of work on plasma-based ultrafast electronics, including earlier demonstrations of nanoscale discharge devices that achieved picosecond switching without semiconductors at all.

Integration is the second pillar of the advance. A pulse generator that lives on the same chip as the memory cell eliminates the parasitic losses of off-chip interconnects, which otherwise smear and attenuate fast pulses before they reach the magnetic element. By placing the nanoplasma switch adjacent to the heterostructure, the researchers ensure that the full amplitude and sharpness of the discharge current arrive where they are needed. This co-location is what makes the approach practical: it converts an impressive physics demonstration into a plausible circuit element. The switch behaves, in effect, as a self-contained nanoscale pulse source that the surrounding CMOS logic can trigger with ordinary voltage signals.

The implications reach beyond memory. Spin–orbit torque switching underlies proposals for magnetic neuromorphic computing, where nanoscale magnets act as artificial neurons, and for non-von Neumann architectures that blur the boundary between storage and computation. If write pulses can be generated on demand in picoseconds with minimal overhead, the operating envelope of such systems widens dramatically. Arrays of nanoplasma-switched magnetic bits could, in principle, be reconfigured at rates approaching terahertz-scale dynamics, although the commentary is careful to note that translating single-device demonstrations into large-scale arrays will require careful management of discharge repeatability, electrode wear and thermal design.

Indeed, the researchers and the commentary both acknowledge the engineering questions that remain. Plasma discharges involve energetic ions and electrons that can erode the electrodes over many switching cycles, and guaranteeing that every pulse has identical amplitude is essential if the switching statistics of the magnetic element are to remain deterministic across billions of operations. Understanding the discharge physics at the nanoscale — how the plasma ignites, how it evolves during the picosecond pulse, and how it decays — will be essential for modeling device lifetimes. The field has already seen rapid progress: recent theoretical and experimental studies of spin–orbit torque, together with related ultrafast switching demonstrations in plasma-based electronics, suggest a maturing toolkit from which robust designs can emerge.

For now, the demonstration marks a conceptual shift. Rather than building ever-faster transistors to drive magnetic devices, engineers can borrow a page from plasma physics and let a nanoscale discharge do the work. The commentary in Nature Electronics frames the result as a step toward ultrafast spintronic systems in which the pulse generation, the magnetic switching and the sensing circuitry all coexist on a single chip. If the remaining reliability and scaling challenges can be met, on-chip nanoplasma switching could become the standard heartbeat of the fastest memories and spin-based processors — devices that write their bits in trillionths of a second while drawing power budgets compatible with mainstream computing.

Subject of Research: On-chip nanoplasma switches generating picosecond current pulses for ultrafast spin–orbit torque switching in ferromagnetic heterostructures

Article Title: On-chip nanoplasma switches for ultrafast spintronics

Article References: Díaz, E. (2026). On-chip nanoplasma switches for ultrafast spintronics. Nature Electronics. https://doi.org/10.1038/s41928-026-01711-3

Image Credits: AI Generated

DOI: 10.1038/s41928-026-01711-3

Keywords: spintronics, nanoplasma, spin–orbit torque, magnetic memory, picosecond pulses, Nature Electronics, ferromagnetic heterostructures, on-chip switching, magnetization reversal, nanoscale devices, MRAM, plasma electronics

Combinatorial optimization is everywhere in modern technology. Designing a chip’s wiring layout, routing vehicles through a delivery network, scheduling flights, and assigning radio frequencies all belong to this family of problems, in which the goal is to find the best configuration from an astronomically large set of possibilities. Many of these tasks are NP-hard, meaning that no known algorithm can solve them efficiently as they grow. Classical computers, built on the von Neumann architecture that separates memory from processing, grind through such problems by evaluating candidate solutions one after another, and the cost quickly becomes prohibitive.

Ising machines take an entirely different approach. They are physical systems engineered to mimic the Ising model, a mathematical framework from statistical physics in which each of many interacting binary variables, called spins, settles into a state that minimizes the total energy of the system. Because any combinatorial optimization problem can be mapped onto such an energy-minimization landscape, a well-engineered Ising machine can let physics do the searching: spins flip stochastically, interact with their neighbors, and collectively relax toward low-energy configurations that correspond to good, and sometimes optimal, solutions. The concept has been realized in quantum annealers, optical platforms built from lasers and fibers, and various electronic chips, each with its own trade-offs between speed, scale, and programmability.

The new machine, which the researchers call VSIM, stands out for the speed at which its spins can change state. At the heart of each spin is a magnetic tunnel junction, the same nanoscale element that stores bits in MRAM. Rather than switching deterministically between two stable states as memory cells do, the device exploits the voltage-controlled magnetic anisotropy effect, in which an applied voltage alters the energy barrier that separates the two magnetic orientations. By tuning the width of a voltage pulse, the team can dial the probability that a single pulse flips the junction anywhere from zero to one hundred percent. That probabilistic switching is exactly what an Ising machine needs, because stochastic spin updates allow the system to escape local energy minima where deterministic algorithms become trapped.

The numbers are striking. Spin updates take between 0.3 and 1 nanosecond, firmly in the sub-nanosecond regime that has eluded most alternative platforms, and each update consumes less than 40 femtojoules per spin. Because the magnetic tunnel junctions are integrated directly with CMOS circuitry, the machine combines the density and manufacturability of standard semiconductor technology with the intrinsic randomness of nanoscale magnetism. The write currents involved are low, another consequence of the voltage-based control mechanism, and the all-to-all connectivity on the chip means any spin can in principle influence any other, which matters greatly for faithfully encoding the interaction structure of real optimization problems.

To demonstrate that the machine is more than a laboratory curiosity, the team mapped two problems drawn directly from electronic design automation, the software domain that underpins the entire semiconductor industry. The first is global routing, the task of deciding how to connect millions of circuit components across a chip’s wiring grid while minimizing wire length and congestion. The second is layer assignment, which determines which of several metal layers each wire segment should occupy. Both are commercially critical steps in chip design, and both were encoded as Ising Hamiltonians and solved on the hardware. In an era when chip design complexity is straining conventional design automation tools, hardware solvers aimed squarely at this workflow have obvious practical appeal.

Benchmarked against standard Max-cut test problems, a canonical yardstick in the Ising machine literature, the chip delivered high-quality solutions at a system-level energy efficiency of 1.92 times ten to the fifth solutions per second per watt. That figure reflects not just the raw speed of the magnetic tunnel junctions but the whole pipeline: an FPGA board configures the couplings, drives the annealing schedule, and reads out the final spin configuration. The researchers also examined how robustly the machine performs in the face of device-to-device variation, an unavoidable reality of nanoscale fabrication, finding that solution quality holds up well provided the single-pulse switching probability stays above roughly sixty percent.

The work lands in a crowded and fast-moving field. Quantum annealers have demonstrated computations on thousands of superconducting qubits, coherent Ising machines built from optical fiber loops have handled 100,000-spin problems, and a parade of CMOS-based annealing chips has appeared at recent circuits conferences. Spintronic approaches, in which the spin itself is the stochastic element, have generally been limited to far smaller arrays. What distinguishes this demonstration is the combination of scale, at 96,000 spins, with sub-nanosecond update speed and full CMOS integration, a trio of attributes that no single previous platform has offered simultaneously.

There remain caveats and open questions. The reported demonstrations, while industrially relevant, are specific problem instances, and scaling the machine to the problem sizes encountered in full-scale chip design will require larger arrays, better coupling precision, and careful management of annealing schedules. The metrics used to compare heterogeneous Ising machines, spanning quantum, optical, and electronic implementations, are themselves still being debated by the community. And like all physics-based solvers, Ising machines provide high-quality solutions rather than guaranteed optima, which may or may not suffice depending on the application.

Even so, the demonstration points toward a future in which the memory devices inside every processor become active computational elements. The voltage-controlled magnetic tunnel junction at the center of this work is already the storage element of a commercial memory technology, which means the path from laboratory demonstration to embedded accelerator runs through established fabrication infrastructure rather than exotic physics. If spintronic Ising machines can keep pace in scale, they could take their place alongside GPUs and dedicated AI accelerators as specialized hardware for the optimization workloads that quietly underpin modern technology. The team has released its source data and code to the community, an invitation for researchers worldwide to stress-test this new class of machine against the hardest problems they can find.

Subject of Research: A CMOS-integrated spintronic Ising machine using magnetoresistive memory for fast, energy-efficient combinatorial optimization

Article Title: An Ising machine for combinatorial optimization based on sub-nanosecond CMOS-integrated magnetoresistive random-access memory

Article References: An Ising machine for combinatorial optimization based on sub-nanosecond CMOS-integrated magnetoresistive random-access memory. (n.d.). https://doi.org/10.1038/s41928-026-01700-6

Image Credits: AI Generated

DOI: 10.1038/s41928-026-01700-6

Keywords: Ising machine, combinatorial optimization, spintronics, MRAM, magnetic tunnel junction, voltage-controlled magnetic anisotropy, Nature Electronics, electronic design automation, global routing, Max-cut, probabilistic computing, CMOS integration

The Ising model, borrowed from statistical physics, describes a collection of spins that can each point up or down, with interactions that either favor alignment or anti-alignment between neighboring spins. Finding the lowest-energy spin configuration of such a system is mathematically equivalent to a broad class of hard combinatorial problems, a correspondence that Andrew Lucas laid out systematically in a widely cited 2014 paper in Frontiers of Physics. Because of this equivalence, researchers have long been interested in building physical systems, so-called Ising machines, whose natural dynamics drive them toward low-energy states, allowing the hardware itself to perform the search that would otherwise demand enormous computational effort from conventional digital machines.

Several approaches to Ising machines have been explored over the past decade. In 2016, two landmark demonstrations appeared in Science: a team led by T. Inagaki and colleagues at NTT built a coherent Ising machine using a network of optical parametric oscillators, while Peter McMahon and collaborators at Stanford University demonstrated a similar photonic architecture with improved scaling and solution quality. These photonic systems showed that physical analog hardware could indeed compete with digital algorithms on certain problem instances, sparking a worldwide effort to find faster, cheaper and more compact physical substrates for Ising-style computation.

Magnetic devices entered this race for compelling reasons. A magnetic tunnel junction consists of two ferromagnetic layers separated by a thin insulating barrier, and its resistance depends on the relative orientation of the two magnetizations, parallel or antiparallel. Those two resistance states map naturally onto the two states of an Ising spin, up or down. Furthermore, each magnetic tunnel junction is, in effect, a tiny bar magnet with genuine thermal fluctuations, a property that Kerem Camsari, Rafatul Faria, Brian Sutton and Supriyo Datta exploited in 2017 in Physical Review X to propose stochastic units called p-bits, probabilistic bits that fluctuate between states with tunable bias and can implement powerful sampling-based optimization and inference algorithms when networked together.

The work highlighted in the new perspective, an article by S. Li and colleagues in Nature Electronics, advances this program by using voltage-controlled magnetic anisotropy to switch and modulate the magnetic tunnel junctions in an integrated array. Voltage-controlled magnetic anisotropy, first demonstrated prominently by W.-G. Wang, M. Li, S. Hageman and C. L. Chien in Nature Materials in 2012, allows the magnetic anisotropy of an ultrathin ferromagnetic film, and hence its energy barrier and preferred magnetization direction, to be tuned by applying a voltage across an adjacent gate dielectric. Because this mechanism acts through an electric field rather than a current, it promises dramatically lower energy per operation than current-based switching schemes, addressing one of the central bottlenecks for scaling magnetic logic and memory technologies.

In the architecture described by the perspective, the integrated array of magnetic tunnel junctions serves as a physical realization of Ising spins, while the coupling between spins, the analog of the exchange interactions in the Ising model, encodes the structure of the optimization problem being solved. By driving the array with appropriate voltage control, the system explores the configuration space and relaxes toward low-energy states that correspond to good, and in favorable cases optimal, solutions of the encoded problem. Crucially, the perspective emphasizes that this can be done quickly and with low energy consumption, two figures of merit that determine whether such hardware can move beyond laboratory demonstrations and into practical use for real workloads in logistics, finance, drug discovery and chip design.

The new report builds on a series of recent advances in magnetic Ising and probabilistic computing hardware. In 2023, Y. Shao and colleagues published work in Nanotechnology on magnetic tunnel junction-based approaches to Ising computation, and in 2024, J. Si and collaborators reported in Nature Communications on magnetic tunnel junction arrays for such applications. More recently, in 2026, M. A. Iftakher and colleagues described related stochastic magnetic computing concepts in Nature Communications. Together, these studies trace a rapid trajectory from single-device physics toward integrated, array-scale systems, and the Li and colleagues work reported in Nature Electronics represents an important consolidation of that progress into a functional, integrated platform for model optimization problems.

What makes the magnetic approach particularly attractive is its compatibility with existing semiconductor manufacturing. Magnetic tunnel junctions are already embedded in billions of consumer devices as memory cells, and the materials and process technology for fabricating them at scale is mature. A computing architecture that repurposes these devices as stochastic optimization elements could, in principle, be fabricated alongside conventional CMOS circuitry, opening a path toward hybrid chips in which a conventional processor offloads hard combinatorial kernels to a dense magnetic Ising fabric. The low switching energies enabled by voltage-controlled magnetic anisotropy further strengthen the case, since the energy cost of each spin update is a key determinant of overall system efficiency at scale.

Challenges nonetheless remain before magnetic Ising machines can challenge state-of-the-art algorithms and specialized processors on production-scale problems. The quality of solutions found by physical relaxations depends on the fidelity of the implemented couplings, the stability and controllability of the stochastic dynamics, the number of spins that can be integrated, and the efficiency of reading out and verifying results. Problems of practical interest often involve far more variables than any near-term chip can host, requiring embedding techniques that inflate problem size, and the performance of Ising machines against the best classical solvers continues to be debated. The authors of the perspective, who are affiliated with the George Washington University and the Physical Measurement Laboratory of the National Institute of Standards and Technology, note that demonstrating clear, reproducible advantages on benchmark problems will be essential for the field’s credibility.

Even so, the demonstration that an integrated array of magnetic tunnel junctions can rapidly and efficiently solve Ising-model optimization problems marks a significant milestone at the intersection of magnetism, memory technology and unconventional computing. It suggests that the devices built to remember bits may also be enlisted to search for them, turning the physics of nanoscale magnetism into a computational resource. As the hardware matures and couples more tightly with conventional electronics, magnetic Ising machines could become a practical accelerator for some of the hardest, most economically consequential computational problems that society routinely faces.

Subject of Research: Using integrated arrays of magnetic tunnel junctions with voltage-controlled magnetic anisotropy to accelerate Ising-model-based combinatorial optimization.

Article Title: Magnetic memory accelerates combinatorial optimization

Article References: Zhang, H., Borders, W. A., & Stiles, M. D. (2026). Magnetic memory accelerates combinatorial optimization. Nature Electronics. https://doi.org/10.1038/s41928-026-01713-1

Image Credits: AI Generated

DOI: 10.1038/s41928-026-01713-1

Keywords: magnetic tunnel junctions, Ising machines, combinatorial optimization, voltage-controlled magnetic anisotropy, Nature Electronics, probabilistic computing, spintronics, low-power computing, p-bits, statistical physics, MRAM, unconventional computing

The central insight is deceptively simple. In conventional pipelines, every incoming pixel is treated as equally worthy of attention, and filtering happens only after expensive operations have already been performed. Convolutional layers multiply and accumulate across entire images, memory is written and read for features that will ultimately be discarded, and in spiking neural networks, neurons fire spikes in response to inputs that a human observer would immediately recognise as irrelevant. Lin and Wang argue that this order of operations is fundamentally wasteful, and that the filtering stage should instead be moved to the very front of the system, into hardware that can reconfigure itself on the fly to gate inputs according to the task at hand.

The hardware concept at the heart of the discussion is the reconfigurable transistor tile. Unlike a fixed-function circuit whose behaviour is frozen at fabrication, a reconfigurable tile can be electrically programmed to perform different logic operations depending on the demands of the moment. In the vision context described in the commentary, such tiles act as an intelligent preprocessing layer: they evaluate incoming visual signals against task-specific criteria and suppress those that fail the test, so that downstream spiking neurons receive only the subset of inputs worth computing on. The result, the authors note, is a reduction in unnecessary activations, which in spiking systems translates directly into fewer spikes, shorter computation times and lower energy draw.

This framing builds on a rich body of prior research in neuromorphic computing, a field whose intellectual roots stretch back to theoretical work on networks of spiking neurons published in the late 1990s. Spiking neural networks differ from conventional deep networks in that information is carried by discrete electrical events, or spikes, exchanged between neuron-like circuits. Because energy in neuromorphic hardware is consumed largely when spikes are generated and propagated, the total spike count is a direct proxy for power consumption. A system that can honestly claim to be efficient must therefore avoid firing spikes for information that does not matter, which is precisely the goal of filtering inputs before computation starts.

The commentary situates the new work within a lineage of advances in Nature Electronics itself. Earlier studies have explored architectures that reduce redundant computation in sparse and event-driven systems, including work published in 2022 on efficient processing strategies for neural networks and a 2023 study on neuromatic approaches to reducing the cost of inference. The 2026 contribution that Lin and Wang discuss extends this trajectory by pushing selectivity away from software and into the physical device layer, where the savings can be realised before signals ever reach the memory-hungry stages of a neural network. In doing so, the work highlights a broader principle in electronics research: efficiency is best achieved not by optimising individual components in isolation, but by rethinking where in the stack each decision is made.

The materials and device community has been moving in this direction for some time. Advances in two-dimensional materials and heterostructures have enabled transistors whose properties can be tuned electrically after fabrication, and researchers have demonstrated reconfigurable devices that switch between distinct logic functions under different gate biases. Other work has explored multifunctional circuits built from van der Waals materials, in which a single device stack can serve as multiple circuit elements depending on configuration. The reconfigurable transistor tile discussed in the commentary can be seen as an architectural expression of this device-level flexibility, arranging programmable elements into a coherent preprocessing fabric that sits between sensors and neural cores.

Why does this matter now? The economics of artificial intelligence at the edge are increasingly unforgiving. Cameras, drones, wearable devices and Internet of Things sensors generate continuous streams of high-resolution data, yet they operate on battery budgets measured in milliwatts. Shipping all of that raw data to cloud data centres for processing is neither energy-efficient nor privacy-preserving, so the filtering and inference must happen locally. Every joule saved in the front end of the pipeline multiplies across billions of cycles. If a reconfigurable front-end layer can eliminate even a fraction of the activations that conventional systems perform, the cumulative savings at the system level could be decisive for always-on applications such as object detection, gesture recognition and environmental monitoring.

Lin and Wang are careful to frame the advance as a step in an ongoing journey rather than a finished destination. The logic-before-spiking paradigm raises questions that the field must still answer: how flexibly can task-relevance criteria be programmed without eroding the energy advantage; how do filtering thresholds adapt when scenes and tasks change dynamically; and how easily can such tiles be integrated with the mainstream CMOS processes on which commercial vision systems depend. The commentary also underscores that benchmarking matters, since efficiency claims in neuromorphic computing are meaningful only when measured against realistic workloads and accounting for the energy cost of the filtering hardware itself.

What makes the approach conceptually compelling is its resonance with biology. Visual systems in animals do not process the retinal image uniformly; attentional mechanisms and early circuitry selectively amplify behaviourally relevant signals while suppressing background clutter long before higher brain areas engage. A camera chip that gates its own inputs according to task relevance, before a single spike is generated, is in a sense importing a principle that evolution arrived at long ago. If reconfigurable hardware can make that principle practical at scale, the result may be artificial intelligence systems that spend their limited energy budgets the way efficient brains do: almost exclusively on the things that matter.

Subject of Research: Reconfigurable hardware that filters task-relevant visual inputs before computation to improve the efficiency of neuromorphic AI systems

Article Title: Filtering inputs for efficient intelligence systems

Article References: Lin, Z., & Wang, Z. (2026). Filtering inputs for efficient intelligence systems. Nature Electronics. https://doi.org/10.1038/s41928-026-01698-x

Image Credits: AI Generated

DOI: 10.1038/s41928-026-01698-x

Keywords: neuromorphic computing, spiking neural networks, reconfigurable transistor tiles, logic-before-spiking computation, visual input filtering, edge AI, energy efficiency, unnecessary activations, Nature Electronics, vision hardware, electronics research, neural networks