Soft Biomaterials Based Flexible Artificial Synapse for Neuromorphic Computing
Tao Guo, Jiawei Ge, Bai Sun, Kangqiang Pan, Zhao Pan, Lan Wei, Yong Yan, Y. Zhou, Yimin A. Wu
Abstract
Abstract Artificial synapses are vital for neuromorphic computing chips that can potentially revolutionize von Neumann systems. Biomaterials‐based bio‐memristors have been investigated as synaptic emulators to develop neuromorphic computing chips due to their biocompatibility, degradability, flexibility, and low costs. However, the existing biomaterials‐based artificial synapses suffer from limited biological synapse functions, insufficient reliability, and poor endurance. Particularly, protein‐based artificial synapse with stable synaptic performances of long‐term potentiation/depression (LTP/LTD) for neuromorphic computing is challenging. Here, the soft material of egg albumen@CuO are employed to develop an artificial synapse. The device can mimic bio‐synaptic functionalities, including the excitatory postsynaptic current (EPSC), spike‐number‐dependent plasticity (SNDP), paired‐pulse facilitation (PPF), and LTP/LTD. High accuracy of 95% has been obtained by the neuromorphic computing simulation for pattern recognition. Combining with density functional theory calculations, multiphysics simulations, and electrical measurements, the analog resistive switching mechanism is attributed to electron hopping. In addition, the device is flexible and can be used to develop wearable systems. The results shed light on the biocompatible and wearable neuromorphic computing chips.