For decades, the dominant architecture of machine vision has kept sensing and computation strictly apart. A camera captures light, converts photons into electrical signals, and ships those signals downstream to a digital processor, where the actual work of interpretation happens. This separation has produced remarkable results, but it comes at a steep energy cost. Every pixel of every video frame must be moved, stored, and multiplied in silicon, and as artificial intelligence systems digest ever-larger streams of visual data, the energy bill of that shuttling has become one of the defining bottlenecks of modern computing. Now, a research team has unveiled a retinomorphic sensor built on a two-dimensional semiconductor heterostructure that collapses this divide, allowing a single device to sense light, process it the way a biological neuron does, and reconfigure its own computational behavior on the fly.

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.

Subject of Research: A multi-responsive retinomorphic sensor based on an Sb2Te3/MoS2 heterostructure enabling reconfigurable optoelectronic computing, photodiode-to-opto-synaptic switching, and neuron-like leaky integrate-and-fire dynamics.

Subject of Research: Technology and Engineering

Article Title: Multi-responsive retinomorphic sensor for reconfigurable optoelectronic computing

Article References: Wang, Y., Cheng, Y., Pan, J., Wang, Y., Sun, J., Liu, Y., Guo, Y., Dun, G., Song, J., Zheng, J., Deng, C., Yang, Y., Li, Y., Wu, F., Dai, Q., Ren, T.-L., & Fang, L. (2026). Multi-responsive retinomorphic sensor for reconfigurable optoelectronic computing. Nature Sensors, 1(7), 591-602. https://doi.org/10.1038/s44460-026-00081-9

Image Credits: AI Generated

DOI: 10.1038/s44460-026-00081-9

Keywords: retinomorphic sensor, optoelectronic computing, Sb2Te3/MoS2 heterostructure, opto-synaptic response, leaky integrate-and-fire neuron, diffractive optics, spiking neural networks, transfer learning, neuromorphic vision, image and video processing, autonomous driving, satellite remote sensing

Cite Scienmag News

APA
MLA
Chicago

Denise Maddox. (September 5, 2026). Retinomorphic sensor adapts optoelectronic computing through multiple stimulus responses. Scienmag. https://scienmag.com/retinomorphic-sensor-adapts-optoelectronic-computing-through-multiple-stimulus-responses/

Copy citation
Download RIS

Tags: adaptive neuromorphic devicesantimony tellurideantimony telluride and molybdenum disulfideartificial neural mimicrybio-inspired neural networksbio-inspired neuromorphic devicesenergy-efficient machine visionintegrated sensing and processinglow-power artificial intelligence sensorsmolybdenum disulfidemulti-stimulus responsemultiple stimulus responseson-chip visual data processingoptoelectronic computingreconfigurable computational behaviorreduction of data shuttling in AI systemsRetinomorphic sensorRetinomorphic sensorstwo-dimensional semiconductor heterostructuretwo-dimensional semiconductor heterostructures