The new work, published in Nature Sensors, centers on a heterostructure formed from antimony telluride and molybdenum disulfide, two layered materials that can be stacked together with atomic precision. The resulting two-terminal device is deceptively simple in appearance: two electrodes, a vertical stack of semiconductors, and nothing else. Yet when the researchers applied a low bias voltage across the terminals, they discovered that the device could be toggled, reversibly and in situ, between two fundamentally different optoelectronic operating modes. In one mode it behaves as a photodiode, generating a current that scales faithfully and linearly with the intensity of incident light, exactly what is needed for conventional image sensing. In the other mode it behaves as an opto-synaptic element, in which light triggers a response that persists, decays, and accumulates over time, mirroring the way a biological synapse integrates incoming spikes before deciding whether to pass a signal onward.

The significance of this switchability is difficult to overstate. In existing optoelectronic computing arrays, each pixel is typically hardwired to a single function. If a designer wants an array that can both sense images and perform synaptic computation, the usual approach is to build separate hardware for each task, multiplying device count, fabrication complexity, and cost. The new sensor sidesteps this trade-off entirely. Because a single pixel can be reconfigured between photodiode and opto-synaptic modes simply by adjusting the applied bias voltage, the same physical array can act as a classical image sensor at one moment and a neuromorphic processing fabric the next. Functional diversity, the researchers argue, no longer has to be purchased at the price of integration scale. The array can grow, and its capabilities can grow with it.

The physics underlying this multi-responsiveness lies in the band alignment between the two constituent materials. Antimony telluride and molybdenum disulfide form a junction whose potential barrier can be modulated by the external bias. At certain bias conditions, photoexcited carriers are swept across the junction in a drift-dominated regime, producing the fast, linear, reset-every-frame response characteristic of a photodiode. Shift the bias, and the transport regime changes: charge generated by light becomes trapped and released slowly at interfacial states, so each optical pulse leaves behind a lingering conductance change that fades over time. It is precisely this fading memory that allows the device to emulate synaptic behavior, with the weight of the “synapse” effectively set by the recent history of illumination.

Perhaps the most striking demonstration in the study is the device’s ability to reproduce the leaky integrate-and-fire dynamics of biological neurons, the canonical model of how real neurons accumulate inputs and spike when a threshold is crossed. When the researchers illuminated the sensor with focused light pulses, the device’s internal state integrated the successive pulses, but also “leaked,” losing accumulated charge between pulses. Once the integrated response reached a threshold, the device fired, producing a discrete output event before resetting. This is not a software simulation of a neuron running on conventional hardware; it is a physical two-terminal device exhibiting the intrinsic dynamics of a neuron in response to light alone. Such light-driven leaky integrate-and-fire behavior is a cornerstone capability for spiking neural networks, a class of computing systems prized for their extreme energy efficiency because they only transmit information when events occur, rather than continuously.

To show that these single-device behaviors could scale into a usable computing system, the team integrated an array of the sensors with diffractive optical components, passive optical elements that shape and route light by diffraction rather than refraction. This combination enabled what the researchers describe as multi-mode optoelectronic computing. In one configuration, the array operated as a conventional imager, capturing scenes for standard image processing. Switching operating modes, the same hardware performed convolution-like operations directly in the optical and optoelectronic domain, preprocessing images before any digital computation was needed. The architecture also handled video streams, where the temporal persistence of the opto-synaptic mode allowed the system to exploit information across frames rather than treating each frame in isolation.

The diffractive components add a crucial spatial dimension to this capability. By patterning light before it reaches the sensor array, they allow certain linear operations, effectively optical convolutions, to be performed at the speed of light and at essentially no energy cost, since passive optics consume no power. The sensor array then transduces and further processes the patterned light in its reconfigurable modes. The result is a computing pipeline in which sensing, analog preprocessing, and neuromorphic event generation are woven together into a single front-end, with the back-end digital processor relieved of the bulk of the workload.

The team went beyond image and video processing to demonstrate transfer learning enabled by optical spike encoding. In this scheme, visual information is converted into trains of optical spikes whose timing and statistics encode features of the input scene. Because the sensor’s synaptic response naturally integrates and fires on these spikes, downstream learning layers can be trained efficiently on the encoded representations and adapted to new tasks without retraining from scratch. This kind of transfer learning, performed on representations extracted physically by the front-end hardware rather than computed digitally, points toward vision systems that can adapt to new environments with minimal additional computation, a property that becomes essential when the computing platform must operate under strict power constraints.

The researchers emphasize that the architecture achieves a superior spatiotemporal dimensionality compared with existing approaches. Conventional image sensors operate in a purely spatial domain, capturing two-dimensional snapshots at fixed frame rates. Neuromorphic event cameras add temporal richness but typically sacrifice conventional imaging capability. The multi-responsive sensor spans both worlds: in photodiode mode it delivers spatial fidelity, while in opto-synaptic and spiking modes it captures temporal dynamics and event-driven information. This expanded representational space, the authors argue, is precisely what demanding real-world applications require, and they point to autonomous driving, satellite remote sensing, and robotics as fields poised to benefit.

The appeal for those domains is concrete. An autonomous vehicle must simultaneously perform conventional object recognition, which favors photodiode-mode imaging, and react to sudden temporal events such as a pedestrian stepping into the road, where spike-based, low-latency processing excels. A satellite remote-sensing platform, with limited power and bandwidth, would gain enormously from a front end that compresses and encodes visual information before transmission. Robots operating in unstructured environments need vision that is fast, adaptive, and frugal with energy. A single sensor platform that can be reconfigured to serve all these roles, without multiplying hardware, could reshape how such systems are engineered.

The broader context for this work is a vigorous international effort to move computation out of the back end and into the front end of vision systems, whether into the sensor itself or into free-space optics. Brain-inspired computing has matured rapidly over the past decade, with memristive devices, phase-change materials, and two-dimensional heterostructures all competing to deliver analog memory and neuromorphic dynamics in compact form factors. What distinguishes the present advance is the combination of simplicity and versatility: a two-terminal device, requiring no complex three-terminal gating or embedded memory elements, that nonetheless offers two distinct photonic operating modes plus intrinsic neuronal firing dynamics, all switchable under low-voltage control.

Challenges remain before such sensors find their way into commercial systems. Array-scale uniformity, long-term endurance under repeated mode switching, integration with readout electronics, and compatibility with large-area fabrication will all need to be demonstrated at production quality. The energy savings promised by front-end computing are only realized in full when the surrounding system, from optics to readout circuits to back-end processors, is co-designed around the sensor’s capabilities. Nevertheless, the demonstration of reversible, in-situ reconfiguration within a single heterostructure marks a meaningful step toward vision hardware that behaves less like a passive camera and more like the retina and early visual cortex it takes as its model.

If the approach scales, the implications extend well beyond efficiency. A sensor that computes blurs the line between perception and cognition, suggesting machines whose first contact with the visual world already carries meaning: edges weighted by context, motion encoded in spike timing, salient events flagged before a single digital multiplication occurs. In a future where cameras multiply into billions of devices, from vehicles to satellites to embedded robots, pushing even a fraction of that interpretive work into the sensor itself could save staggering amounts of energy. The retinomorphic sensor described in this study offers a glimpse of that future, one in which the eye does not merely see but begins, at the moment of seeing, to think.

Traditional artificial intelligence hardware architectures typically bifurcate these essential functions—detection, memory retention, and processing—forcing data to shuttle between distinct components. This separation not only introduces latency but significantly amplifies energy consumption. The novel phototransistor developed by the OSU team disrupts this paradigm by embedding memory capabilities precisely where sensory inputs occur, vastly improving computational efficiency and speed.

Central to this device’s innovation is its ability to mimic the neurochemical processes governing memory strength and decay in the human brain. Rather than storing information with fixed permanence, the phototransistor utilizes trapped electrical charges generated by incident light to represent memories, which can then be pharmacologically tuned—via electrical gate voltages—to either reinforce or weaken these stored traces over time. This dynamic modulation reproduces the adaptive nature of biological synapses and opens unprecedented avenues for creating AI systems that can ‘forget’ as well as ‘remember,’ essential for real-time learning and adaptation.

Structurally, the device marries an oxide semiconductor acting as the electronic transistor channel with an organic photosensitive layer responsible for absorbing photons and generating charge carriers. The organic layer traps a subset of these carriers, creating a persistent local electric field even after illumination ceases. This trapped charge influences electron flow through the semiconductor channel, effectively encoding a memory of prior light exposure.

A key technical breakthrough lies in the device’s gate-tunable interface: by adjusting the voltage applied to the transistor’s gate terminal, researchers can manipulate the spatial positioning of trapped charges relative to the conduction channel at a nanoscopic level. This precise control modulates the strength of electrical interaction, allowing for programmable decay times of the optoelectronic memory—ranging from seconds to considerably longer durations. Such flexibility is vital for neuromorphic systems requiring adjustable time constants for processing temporal patterns.

The implications of this technology extend beyond simple visual sensing. By localizing both sensing and adaptive memory functions, the phototransistor can serve as a foundational building block for advanced vision systems capable of real-time data processing with exceptional energy efficiency. This novel hardware approach offers substantial improvements over conventional sensors, which often offload processing to centralized units, incurring latency and energy penalties.

Neuromorphic computing, a field striving to emulate the architecture and operational principles of the brain, stands to gain significantly from this development. The device’s capacity to embody synapse-like plasticity within a phototransistor embodies a leap toward systems that do not merely compute but adapt, learn, and optimize autonomously. Furthermore, the integration of these functionalities at the hardware level paves the way for compact, scalable AI platforms potentially transformative for robotics, autonomous vehicles, and sensory-rich IoT devices.

The research team acknowledges that the trapped charges’ mobility within the device’s photosensitive layer marks a fundamental departure from fixed-charge memory devices. By enabling charge repositioning in response to externally applied voltages, the phototransistor achieves an unprecedented degree of control over memory retention timescales—a property rarely realized in optoelectronic components designed for AI applications.

From an energy perspective, co-localizing sensing and computation reduces data transfer bottlenecks traditionally plaguing AI processors. This attribute directly addresses pressing challenges in AI hardware, where escalating computational demands often collide with limitations in battery life and thermal management. As a result, the novel phototransistor aligns with broader efforts to develop sustainable, high-performance AI technologies.

Conceived through a multidisciplinary collaboration spanning electrical engineering and physical sciences at Oregon State University, the device’s development also underscores the growing convergence of materials science with computational intelligence. By synergizing metal oxide semiconductors with organic tetracene compounds, the team harnessed complementary material properties—robust electron transport with efficient light absorption—to realize the device’s multifunctional capabilities.

The National Science Foundation’s support, alongside the efforts of researchers including Larry Cheng, Ahasan Ullah, Tasnim Sarker, Xueqiao Zhang, Andrew Ensinger, Lizhong Chen, Roshell Lamug, and Oksana Ostroverkhova, culminated in the publication of their findings in the esteemed journal Advanced Functional Materials. The study not only charts new territory in optoelectronics but also sets the stage for transformative innovation in neuromorphic computing hardware.

As artificial intelligence increasingly demands systems capable of real-world adaptability and resource efficiency, innovations like this gate-tunable neuro-phototransistor epitomize the direction forward. By drawing directly from the brain’s principles of memory modulation and energy-efficient computation, the OSU team’s technology heralds a new era where AI devices can perceive, process, and remember in ways that approximate human cognition more closely than ever before.

Subject of Research: Not applicable

Article Title: Neuromodulator-Inspired Gate-Tunable Tetracene–Metal Oxide Phototransistor for Adaptive Optoelectronic Memory and Neuromorphic Computing

News Publication Date: 19-May-2026

Web References: http://dx.doi.org/10.1002/adfm.75942

References: Published in Advanced Functional Materials

Image Credits: Oregon State University

Keywords

Neuromorphic computing, phototransistor, adaptive memory, optoelectronics, brain-inspired AI, gate-tunable memory, oxide semiconductor, organic photosensitive material, energy-efficient AI, in-sensor computing, tetracene, neuromodulation