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

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