Charge‐Trap Memory with Engineered Temporal Dynamics for Physically Integrated Reservoir Computing
Mengfan Wu, Ziqi Chen, Niannian Yu, Linfeng Li, Xinhao Zhang, Xinyi Wan, Yi Zheng, Shuaishuai Xu, Yang Liu, Jiawei Peng, Yao Wang, Junhui Yuan, Jiafu Wang, Xuewen Wang · Small Science · 2025
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 PdSe 2 charge‐trap memory (CTM) by ultrafast photoexcitation is demonstrated, which directly generates PdSe 2‐x O x nanodefects, converting volatile states (≈0% retention) into nonvolatile states (≈80% retention) by introducing electron‐depleting defects and scattering centers in PdSe 2 channel. This engineering extends relaxation time constants from 15.6 s to 99.4 s and enables multilevel memory (>2 6 levels) with prolonged retention (>2000 s). Leveraging dual nonlinear/stable operational modes, the physically integrated RC system achieves 91.7% (MNIST) and 93.3% (spoken digits) classification accuracy. Notably, it pioneers electrocardiogram arrhythmia detection (N, L, R, A, and V classes) with 92.3% accuracy, surpassing existing in‐memory computing approaches. By establishing a defect engineering paradigm for material‐intrinsic neuromorphic devices, this work advances energy‐efficient AI hardware for biomedical diagnostics and edge computing applications.