Pressure‐Adaptive Artificial Synapses with High Linearity for Intelligent Computing in Extreme Environments
Abstract
Abstract The ongoing exploration of the physical world has intensified the demand for intelligent computing in extreme environments. However, intelligent devices operating under extreme high‐pressure conditions are limited by the pressure tolerance of the materials used for intelligent computing. A pressure‐adaptive artificial synapse (PAAS) using VO 2 (M 1 ) nanoparticles is developed, leveraging the increased lattice rigidity during the M 1 ‐to‐M 1 ’ phase transition (1 atm to 15.1 GPa), which causes the photoinduced insulator‐to‐metal transition to be Mott dominated. The PAAS demonstrated a stable operating current, a superior biomimetic plasticity (maximum paired‐pulse facilitation index from 109.6% to 155.4%), and an improved postsynaptic current linearity (Pearson's r from 0.64 to 0.97) from 1 atm to 15.1 GPa. Furthermore, an artificial neural network mapped by PAAS under high pressure achieved a validation accuracy of 95%–97% in handwritten digit recognition. The PAAS is also applied to a convolutional autoencoder for denoising reconstruction of color images.
Article Details
Authors (5)
Yang Wang
Chen‐Yang Zhang
State Key Laboratory of High Pressure and Superhard Materials, College of Physics Jilin University Changchun 130012 China
Shun‐Xin Li
State Key Laboratory of High Pressure and Superhard Materials, College of Physics Jilin University Changchun 130012 China
Guanjun Xiao
State Key Laboratory of Superhard Materials, College of Physics
Bo Zou
State Key Laboratory of High Pressure and Superhard Materials, College of Physics