In Advanced materials (Deerfield Beach, Fla.)
Neurobiological circuits containing synapses can process signals while learning concurrently in real-time. Before an artificial neural network (ANN) can execute a signal-processing program, it must first be programmed by humans or trained with respect to a large and defined data set during learning processes, resulting in significant latency, high power consumption, and poor adaptability to unpredictable changing environments. In this article, we report a crossbar circuit of synaptic resistors (synstors), each synstor integrating an Si channel with an Al oxide memory layer and Ti silicide Schottky contacts. Individual synstors were characterized and analyzed to understand their concurrent signal-processing and learning abilities. Without any prior training, synstor circuits concurrently execute signal-processing and learning in real time to fly drones toward a target position in an aerodynamically changing environment faster than human controllers, and with learning speed, performance, power consumption, and adaptability to the environment significantly superior to an ANN running on computers. The synstor circuit provides a path to establish power-efficient intelligent systems with real-time learning and adaptability in the capriciously mutable real-world. This article is protected by copyright. All rights reserved.
Gao Dawei, Shenoy Rahul, Yi Suin, Lee Jungmin, Xu Mingjie, Rong Zixuan, Deo Atharva, Nathan Dhruva, Zheng Jian-Guo, Williams R Stanley, Chen Yong
2023-Feb-13
Al oxide, Ti silicide, artificial intelligence systems, concurrent learning and signal-processing, synaptic resistor circuits
