Self-Rectifying Switching Memory Based on HfOx/FeOx Semiconductor Heterostructure for Neuromorphic Computing

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 HfOx/FeOx semiconductor heterojunction SR-RRAM with a robust self-rectifying switching behavior featured by an average rectifying ratio (≈104), high resistance ratio (>106), high cycling endurance (>104 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.