Pressure‐Adaptive Artificial Synapses with High Linearity for Intelligent Computing in Extreme Environments

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 VO₂ (M₁) nanoparticles is developed, leveraging the increased lattice rigidity during the M₁-to-M₁' 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.