Affiliation:
1. Key Laboratory of Brain‐Like Neuromorphic Devices and Systems of Hebei Province Hebei Key Laboratory of Photo‐Electricity Information and Materials Hebei University Baoding 071002 China
2. Department of Materials Science and Engineering National University of Singapore Singapore 117576 Singapore
Abstract
AbstractTraditional artificial vision systems built using separate sensing, computing, and storage units have problems with high power consumption and latency caused by frequent data transmission between functional units. An effective approach is to transfer some memory and computing tasks to the sensor, enabling the simultaneous perception‐storage‐processing of light signals. Here, an optical–electrical coordinately modulated memristor is proposed, which controls the conductivity by means of polarization of the 2D ferroelectric Ruddlesden–Popper perovskite film at room temperature. The residual polarization shows no significant decay after 109‐cycle polarization reversals, indicating that the device has high durability. By adjusting the pulse parameters, the device can simulate the bio‐synaptic long/short‐term plasticity, which enables the control of conductivity with a high linearity of ≈0.997. Based on the device, a two‐layer feedforward neural network is built to recognize handwritten digits, and the recognition accuracy is as high as 97.150%. Meanwhile, building optical–electrical reserve pool system can improve 14.550% for face recognition accuracy, further demonstrating its potential for the field of neural morphological visual systems, with high density and low energy loss.
Funder
National Natural Science Foundation of China
Cultivation Fund of the Key Scientific and Technical Innovation Project, Ministry of Education
National Major Science and Technology Projects of China