Self‐Rectifying Switching Memory Based on HfO<i><sub>x</sub></i>/FeO<i><sub>x</sub></i> Semiconductor Heterostructure for Neuromorphic Computing
Haofeng Ran, Zhijun Ren, Jie Li, Bai Sun, Tongyu Wang, Dengshun Gu, Wenhua Wang, Xiaofang Hu, Zhekang Dong, Qunliang Song, Lidan Wang, Shukai Duan, Guangdong Zhou
Abstract
Abstract Sneak‐path current is one of the biggest barriers for large‐scale passive memristor array integration. An ideal self‐rectifying resistance random access memory (SR‐RRAM) is a desirable solution but it has not been demonstrated today for optimizing comprehensive indexes for neuromorphic computing. The HfO x /FeO x semiconductor heterojunction SR‐RRAM with a robust self‐rectifying switching behavior featured by an average rectifying ratio (≈10 4 ), high resistance ratio (>10 6 ), high cycling endurance (>10 4 cycles), high computing precision (>6 bits) and synaptic plasticity such as paired‐pulse facilitation (PPF) and the spike‐timing‐dependent plasticity (STDP) for artificial intelligence recognition is developed using the unidirectional conductivity feature of p‐n junction. The electron hopping, tunneling, and blocking in this semiconductor heterojunction that is verified by the energy band mode based on UV photoelectron spectroscopy (UPS) technology and low‐energy inverse photoelectron spectroscopy (LEIPS) and in situ high resolution transmission electron microscopy (HR‐TEM) observation plays a dominant role in the self‐rectifying analog switching behaviors. This work provides energy‐band engineering for the large‐scale memristor array integration, representing a significant advancement in hardware for neuromorphic computing.