Si/CuO Heterojunction-Based Photomemristor for Reconfigurable, Non-Volatile, and Self-Powered In-Sensor Computing.

In-sensor computing has attracted considerable interest as a solution for overcoming the energy efficiency and response time limitations of the traditional von Neumann architecture. Recently, emerging memristors based on transition-metal oxides (TMOs) have attracted attention as promising candidates for in-memory computing owing to their tunable conductance, high speed, and low operational energy. However, the poor photoresponse of TMOs presents challenges for integrating sensing and processing units into a single device. This integration is crucial for eliminating the need for a sensor/processor interface and achieving energy-efficient in-sensor computing systems. In this study, a Si/CuO heterojunction-based photomemristor is proposed that combines the reversible resistive switching behavior of CuO with the appropriate optical absorption bandgap of the Si substrate. The proposed photomemristor demonstrates a simultaneous reconfigurable, non-volatile, and self-powered photoresponse, producing a microampere-level photocurrent at zero bias. The controlled migration of oxygen vacancies in CuO result in distinct energy-band bending at the interface, enabling multiple levels of photoresponsivity. Additionally, the device exhibits high stability and ultrafast response speed to the built-in electric field. Furthermore, the prototype photomemristor can be trained to emulate the attention-driven nature of the human visual system, indicating the tremendous potential of TMO-based photomemristors as hardware foundations for in-sensor computing.

PbS/CsPbBr3 Heterojunction for Broadband Neuromorphic Vision Sensing.

Neuromorphic light sensors with analogue-domain image processing capability hold promise for overcoming the energy efficiency limitations and latency of von Neumann architecture-based vision chips. Recently, metal halide perovskites, with strong light-matter interaction, long carrier diffusion length, and exceptional photoelectric conversion efficiencies, exhibit reconfigurable photoresponsivity due to their intrinsic ion migration effect, which is expected to advance the development of visual sensors. However, suffering from a large bandgap, it is challenging to achieve highly tunable responsivity simultaneously with a wide-spectrum response in perovskites, which will significantly enhance the image recognition accuracy through the machine learning algorithm. Herein, we demonstrate a broadband neuromorphic visual sensor from visible (Vis) to near-infrared (NIR) by coupling all-inorganic metal halide perovskites (CsPbBr3) with narrow-bandgap lead sulfide (PbS). The PbS/CsPbBr3 heterostructure is composed of high-quality single crystals of PbS and CsPbBr3. Interestingly, the ion migration of CsPbBr3 with the implementation of an electric field induces the energy band dynamic bending at the interface of the PbS/CsPbBr3 heterojunction, leading to reversible, multilevel, and linearly tunable photoresponsivity. Furthermore, the reconfigurable and broadband photoresponse in the PbS/CsPbBr3 heterojunction allows convolutional neuronal network processing for pattern recognition and edge enhancements from the Vis to the NIR waveband, suggesting the great potential of the PbS/CsPbBr3 heterostructure in artificial intelligent vision sensing.