Pressure-Adaptive Artificial Synapses with High Linearity for Intelligent Computing in Extreme Environments.
basic_science · Level V
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- Record sourced from PubMed, PMID 41409031.
- Also identified by DOI 10.1002/adma.202516053.
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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<sub>2</sub> (M<sub>1</sub>) nanoparticles is developed, leveraging the increased lattice rigidity during the M<sub>1</sub>-to-M<sub>1</sub>' 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.