Charge-Trap Memory with Engineered Temporal Dynamics for Physically Integrated Reservoir Computing.

ABSTRACT: 2D material (2DM)-based reservoir computing (RC) systems combine the advantages of low-power hardware implementation with lightweight neural network architectures capable of processing complex temporal patterns through minimal training overhead, positioning them as ideal platforms for edge artificial intelligence (AI) applications. Here, a homogeneous RC system via defect engineering in PdSe₂ charge-trap memory (CTM) by ultrafast photoexcitation is demonstrated, which directly generates PdSe_2-xO_x nanodefects, converting volatile states (≈0% retention) into nonvolatile states (≈80% retention) by introducing electron-depleting defects and scattering centers in PdSe₂ channel. This engineering extends relaxation time constants from 15.6 s to 99.4 s a

SUBMITTER: Wu M 

PROVIDER: S-EPMC12697835 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature