Neuro-inspired vision systems hold great promise to address the growing demands of mass data processing for edge computing, a distributed framework that brings computation and data storage closer to the sources of data. In addition to the capability of static image sensing and processing, the hardware implementation of a neuro-inspired vision system also requires the fulfilment of detecting and recognizing moving targets. Here, we demonstrated a neuro-inspired optical sensor based on two-dimensional NbS₂/MoS₂ hybrid films, which featured remarkable photo-induced conductance plasticity and low electrical energy consumption. A neuro-inspired optical sensor array with 10 × 10 NbS₂/MoS₂ phototransistors enabled highly integrated functions of sensing, memory, and contrast enhancement capabilities for static images, which benefits convolutional neural network (CNN) with a high image recognition accuracy. More importantly, in-sensor trajectory registration of moving light spots was experimentally implemented such that the post-processing could yield a high restoration accuracy. Our neuro-inspired optical sensor array could provide a fascinating platform for the implementation of high-performance artificial vision systems.

Two-dimensional (2D) layered semiconductors with nonlinear optical (NLO) properties hold great promise to address the growing demand of multifunction integration in electronic-photonic integrated circuits (EPICs). However, electronic-photonic co-design with 2D NLO semiconductors for on-chip telecommunication is limited by their essential shortcomings in terms of unsatisfactory optoelectronic properties, odd-even layer-dependent NLO activity and low NLO susceptibility in telecom band. Here we report the synthesis of 2D SnP₂Se₆, a van der Waals NLO semiconductor exhibiting strong odd-even layer-independent second harmonic generation (SHG) activity at 1550 nm and pronounced photosensitivity under visible light. The combination of 2D SnP₂Se₆ with a SiN photonic platform enables the chip-level multifunction integration for EPICs. The hybrid device not only features efficient on-chip SHG process for optical modulation, but also allows the telecom-band photodetection relying on the upconversion of wavelength from 1560 to 780 nm. Our finding offers alternative opportunities for the collaborative design of EPICs.

Neuromorphic hardware for artificial general vision intelligence holds the potential to match and surpass biological visual systems by processing complex visual dynamics with high adaptability and efficiency. However, current implementations rely on multiple complementary metal-oxide-semiconductor or neuromorphic elements, leading to significant area and power inefficiencies and system complexity. This is owing to a key challenge that no single electronic device, to our knowledge, has yet been demonstrated that can integrate retina-like and cortex-like spiking and graded neuronal dynamics operable across both optical and electrical domains. Here we report a single ultra-adaptive neuromorphic vision device (I_xT_yO_1-x-y/CuO_x/Pd) by ingeniously tailoring its electronic properties, enabling uniquely controlled interface and bulk dynamics by charged particles, including electrons, oxygen ions and vacancies. The device highly amalgamates broadband retinal spiking neuron and non-spiking graded neuron, and cortical synapse and neuron dynamics, with ultralow power consumption. Real-time optoelectronic dynamics is elucidated through in situ scanning transmission electron microscopy and validated by technology computer-aided design simulations. An artificial general vision intelligence system based on homogeneous ultra-adaptive neuromorphic vision device arrays is constructed, adaptively supporting both asynchronous event-driven and synchronous frame-driven paradigms for versatile cognitive imaging demands, with superior power efficiency of up to 67.89 trillion operations per second per watt and area efficiency of up to 3.96 mega operations per second per feature size (MOPS/F²).