Retinomorphic devices beyond silicon for dynamic machine vision, Xia, Yuxin, Babu, Roshni Satheesh, Vishwanath, Sujaya Kumar, Georgiadou, Dimitra G

Contier, O., Baker, C. I. & Hebart, M. N. Distributed representations of behaviour-derived object dimensions in the human visual system. Nat. Hum. Behav. 8, 2179–2193 (2024). Chen, J. et al. Optoelectronic graded neurons for bioinspired in-sensor motion perception. Nat. Nanotechnol. 18, 882–888 (2023). Zhang, Z. et al. All-in-one two-dimensional retinomorphic hardware device for motion detection and recognition. Nat. Nanotechnol. 17, 27–32 (2022). Li, T. et al. Reconfigurable, non-volatile neuromorphic photovoltaics. Nat. Nanotechnol. 18, 1303–1310 (2023). Wang, Z., Wan, T., Ma, S. & Chai, Y. Multidimensional vision sensors for information processing. Nat. Nanotechnol. 19, 919–930 (2024). Bian, L. et al. A broadband hyperspectral image sensor with high spatio-temporal resolution. Nature 635, 73–81 (2024). Zhou, Y. et al. Computational event-driven vision sensors for in-sensor spiking neural networks. Nat. Electron. 6, 870–878 (2023). Huang, H. et al. Fully integrated multi-mode optoelectronic memristor array for diversified in-sensor computing. Nat. Nanotechnol. 20, 93–103 (2024). Liao, F. et al. Bioinspired in-sensor visual adaptation for accurate perception. Nat. Electron. 5, 84–91 (2022). Mennel, L. et al. Ultrafast machine vision with 2D material neural network image sensors. Nature 579, 62–66 (2020). Gehrig, D. & Scaramuzza, D. Low-latency automotive vision with event cameras. Nature 629, 1034–1040 (2024). Lee, J...

A retinomorphic hardware with adaptive imaging and retinal processing functions is realized for embodied intelligent vision.

Nature Nanotechnology, Published online: 23 July 2025; doi:10.1038/s41565-025-01973-6A photodiode with bio-plausible event sensing, filtering and adaptation behaviours to replicate the retinal nanostructure and visual pathway is presented, enabling consistent and high-performance machine vision under extreme lighting.

Lu, D. et al. Monolithic three-dimensional tier-by-tier integration via van der Waals lamination. Nature 630, 340–345 (2024). Kang, J.-H. et al. Monolithic 3D integration of 2D materials-based electronics towards ultimate edge computing solutions. Nat. Mater. 22, 1470–1477 (2023). Wu, G. et al. Ferroelectric-defined reconfigurable homojunctions for in-memory sensing and computing. Nat. Mater. 22, 1499–1506 (2023). Zhang, Z. et al. All-in-one two-dimensional retinomorphic hardware device for motion detection and recognition. Nat. Nanotechnol. 17, 27–32 (2021). Pi, L. et al. Broadband convolutional processing using band-alignment-tunable heterostructures. Nat. Electron. 5, 248–254 (2022). Zhou, T. et al. Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit. Nat. Photon. 15, 367–373 (2021). Huang, L. et al. Spectral imaging with deep learning. Light Sci. Appl. 11, 61 (2022). Wang, Z. et al. Multidimensional vision sensors for information processing. Nat. Nanotechnol. 19, 919–930 (2024). Jang, H. et al. In-sensor optoelectronic computing using electrostatically doped silicon. Nat. Electron. 5, 519–525 (2022). Yang, Y. et al. In-sensor dynamic computing for intelligent machine vision. Nat. Electron. 7, 225–233 (2024). Choi, C. et al. Reconfigurable heterogeneous integration using stackable chips with embedded artificial intelligence. Nat. Electron. 5, 386–393 (2022). Chaves, A. et al. Bandgap engineering of two-dimensional semiconductor materials. npj...

The structure and functions of the retinomorphic photodiodide replicating the retinal visual pathway. Credit: Nature Nanotechnology (2025). DOI: 10.1038/s41565-025-01973-6 Over the past decades, computer scientists have developed increasingly sophisticated sensors and machine learning algorithms that allow computer systems to process and interpret images and videos. This tech-powered capability, also referred to as machine vision, is proving to be highly advantageous for the manufacturing and production of food products, drinks, electronics, and various other goods. Machine vision could enable the automation of various tedious steps in industry and manufacturing, such as the detection of defects, the inspection of electronics, automotive parts or other items, the verification of labels or expiration dates and the sorting of products into different categories.

Yi et al. introduce zero-bias lead sulfide quantum dot retinomorphic sensors that convert shortwave infrared brightness changes into transient electrical spikes using solution-processed thin films. Integrated with silicon readout circuits, these sensors enable event-based imaging that detects motion while suppressing backgrounds, providing low-power, data-efficient vision for autonomous systems and surveillance.

2D ferroelectrics materials enabling non-volatile polarization memory, optical excitability, and neuromorphic processing within a unified material and provides a mechanistic analysis of polarization-induced band modulation, including photon-assisted domain reorientation, switching kinetics, and interfacial dipole coupling that governs resistive switching and optoelectronic synaptic in 2D ferroelectrics. 2D ferroelectric memristors, FeFETs, and Fe-Memtransistors are emphasized on light-tunable multilevel state, ultralow switching energies, and neuromorphic visual capabilities. ABSTRACT 2D ferroelectric materials have recently emerged as a promising class of atomically thin semiconductors capable of integrating sensing, memory, and computation within a single device. Their unique combination of spontaneous switchable polarization, strong light-matter coupling, and van der Waals (vdW) interface compatibility provides an ideal platform for next-generation optoelectronic vision sensors. Coupling ferroelectric polarization with photoresponse, 2D ferroelectric materials such as α -In 2 Se 3 , CuInP 2 S 6 (CIPS), SnS, and WTe 3 enable non-volatile modulation of photocarrier transport, facilitating adaptive visual perception analogous to the human retina. These 2D ferroelectric photonic devices demonstrate synaptic plasticity, short-term and long-term memory, and optical potentiation and depression characteristics under visible and near-infrared excitation. Integrating ferroelectricity into optoelectronic architectures addresses the von-Neumann bottleneck by enabling in-sensor computing, where data are sensed, stored, and processed locally, minimizing latency and energy consumption. This review provides a comprehensive overview of 2D ferroelectric materials and their device architectures in the memristive and memtransistors devices structures for optoelectronic vision sensors, highlighting their polarization mechanism, light-driven conductance modulation, and neuromorphic functionalities. Additionally, current challenges, such as scalability, polarization fatigue, and interface engineering, have also been extensively discussed together with heterostructure design and hybrid ferroelectric-semiconductor integration toward energy-efficient bio-inspired vision systems.

This study introduces retinomorphic active-matrix synaptic phototransistor array based on metal oxide/organic semiconductor heterojunctions, incorporating in-sensor chromatic color-opponent processing. The dual photogates enable synaptic color-opponent processing with a high current dynamic range exceeding 90 dB. The array enables spatial chromatic contrast imaging and dynamic trajectory capture for efficient artificial color perception. Abstract The human retina perceives and preprocesses the spectral information of incident light, enabling fast image recognition and efficient chromatic adaptation. In comparison, it is reluctant to implement parallel spectral preprocessing and temporal information fusion in current complementary metal-oxide-semiconductor (CMOS) image sensors, requiring intricate circuitry, frequent data transmission, and color filters. Herein, an active-matrix synaptic phototransistor array (AMSPA) is developed based on organic/inorganic semiconductor heterostructures. The AMSPA provides wavelength-dependent, bidirectional photoresponses, enabling dynamic imaging and in-sensor spectral preprocessing functions. Specifically, near-infrared light induces inhibitory photoresponse while UV light results in exhibitory photoresponse. With rational structural design of the organic/inorganic hybrid heterostructures, the current dynamic range of phototransistor is improved to over 90 dB. Finally, a 32 × 64 AMSPA (128 pixels per inch) is demonstrated with one-switch-transistor and one-synaptic phototransistor (1-T-1-PT) structure, achieving spatial chromatic enhancement and temporal trajectory imaging. These results reveal the feasibility of AMSPA for constructing artificial vision systems.

A hemispherical optoelectronic memristive array is demonstrated, which relies on localized surface plasmon resonance (LSPR) effect in Ag-TiO 2 nanocluster /sodium alginate nanocomposite. Both fully light-modulated synaptic plasticity and wide field of view can be implemented in the hemispherical array. Furthermore, depth perception and motion detection based on binocular disparity have been demonstrated by constructing two retinomorphic arrays. Abstract Binocular stereo vision relies on imaging disparity between two hemispherical retinas, which is essential to acquire image information in three dimensional environment. Therefore, retinomorphic electronics with structural and functional similarities to biological eyes are always highly desired to develop stereo vision perception system. In this work, a hemispherical optoelectronic memristor array based on Ag-TiO 2 nanoclusters/sodium alginate film is developed to realize binocular stereo vision. All-optical modulation induced by plasmonic thermal effect and optical excitation in Ag-TiO 2 nanoclusters is exploited to realize in-pixel image sensing and storage. Wide field of view (FOV) and spatial angle detection are experimentally demonstrated owing to the device arrangement and incident-angle-dependent characteristics in hemispherical geometry. Furthermore, depth perception and motion detection based on binocular disparity have been realized by constructing two retinomorphic memristive arrays. The results demonstrated in this work provide a promising strategy to develop all-optically controlled memristor and promote the future development of binocular vision system with in-sensor architecture.

This work demonstrates a retinomorphic prototype that integrates ferroelectricity and photosensitivity within a two-dimensional (2D) α-In 2 Se 3 material, emulating simultaneous functions in the human retina, that is, perceptive light-sensing, memory, and computation. The optoelectronic memory efficiently classifies 12 000 images with a satisfactory precision of 94% and replicates five convolutional kernels using a network of 3 × 3 phototransistors for the image processing task. Abstract The development of all-in-one devices for artificial visual systems offers an attractive solution in terms of energy efficiency and real-time processing speed. In recent years, the proliferation of smart sensors in the growth of Internet-of-Things (IoT) has led to the increasing importance of in-sensor computing technology, which places computational power at the edge of the data-flow architecture. In this study, a prototype visual sensor inspired by the human retina is proposed, which integrates ferroelectricity and photosensitivity in two-dimensional (2D) α-In 2 Se 3 material. This device mimics the functions of photoreceptors and amacrine cells in the retina, performing optical reception and memory computation functions through the use of electrical switching polarization in the channel. The gate-tunable linearity of excitatory and inhibitory functions in photon-induced short-term plasticity enables to encode and classify 12 000 images in the Mixed National Institute of Standards and Technology (MNIST) dataset with remarkable accuracy, achieving ≈94%. Additionally, in-sensor convolution image processing through a network of phototransistors, with five convolutional kernels electrically pre-programmed into the transistors is demonstrated. The convoluted photocurrent matrices undergo straightforward arithmetic calculations to produce edge and feature-enhanced scenarios. The findings demonstrate the potential of ferroelectric α-In 2 Se 3 for highly compact and efficient retinomorphic hardware implementation, regardless of ambipolar transport in the channel.

Organic retinomorphic sensors offer the advantage of in-sensor processing to filter out the redundant static background. To improve this promising structure, incorporating appropriate interfacial layers around the photoactive layer extends the carrier lifetime. Compared to its photodiode counterpart, the retinomorphic sensor shows better detectivity and response speed due to the additional insulating layer to enable efficient motion tracking. Abstract Organic retinomorphic sensors offer the advantage of in-sensor processing to filter out redundant static backgrounds and are well suited for motion detection. To improve this promising structure, here, the key role of interfacial energetics in promoting charge accumulation to raise the inherent photoresponse of the light-sensitive capacitor is studied. Specifically, incorporating appropriate interfacial layers around the photoactive layer is crucial to extend the carrier lifetime, as confirmed by intensity-modulated photovoltage spectroscopy. Compared to its photodiode counterpart, the retinomorphic sensor shows better detectivity and response speed due to the additional insulating layer, which reduces the dark current and the RC time constant. Lastly, three retinomorphic sensors are integrated into a line array to demonstrate the detection of movement speed and direction, showing the potential of retinomorphic designs for efficient motion tracking.