Graphene Oxide Quantum Dots Based Memristors with Progressive Conduction Tuning for Artificial Synaptic Learning

Abstract

AbstractMemristors as electronic artificial synapses have attracted increasing attention in neuromorphic computing. Emulation of both “learning” and “forgetting” processes requires a bidirectional progressive adjustment of memristor conductance, which is a challenge for cutting‐edge artificial intelligence. In this work, a memristor device with a structure of Ag/Zr0.5Hf0.5O2:graphene oxide quantum dots/Ag is presented with the feature of bidirectional progressive conductance tuning. The conductance of proposed memristor is adjusted through voltage pulse number, amplitude, and width. A series of voltage pulses with an amplitude of 0.6 V and a width of 30 ns is enough to modulate conductance. The impacts of pulses with different parameters on conductance modulation are investigated, and the potential relationship between pulse amplitude and energy is revealed. Furthermore, it is proved that the pulse with low energy can realize the almost linear conductance regulation, which is beneficial to improve the accuracy of pattern recognition. The bidirectional progressive conduction modulation mimics various plastic synapses, such as spike‐timing‐dependent plasticity and paired‐pulse facilitation. This progressive conduction tuning mechanism might be attributed to the coexistence of tunneling effect and extrinsic electrochemical metallization effect. This work provides one way for memristor to attain attractive features such as bidirectional tuning, low‐power consumption, and fast speed switching that is in urgent demand for further evolution of neuromorphic chips.