Compact Model of HfOX-Based Electronic Synaptic Devices for Neuromorphic Computing

HfOx-based resistive switching device has been explored as one of the promising candidates for the electronic synapses of neuromorphic computing systems due to its high performance, low cost, and compatibility with CMOS technology. To meet the codesign requirement of HfOx-based electronic synapses with CMOS neurons in the neuromorphic computing systems, a compact model that can capture the synaptic futures of HfOx-based resistive switching device is developed. The developed model can accurately describe the multilevel conductance transition behaviors during RESET process for depression learning as well as the binary stochastic transition behavior during SET process for potentiation learning. After the verification with experimental data, the model is used to simulate a winner-take-all neural network to classify patterns with unsupervised competitive learning algorithm. Simulation results imply that the average recognition accuracy would decrease with the increase of the resistance variation of low resistance state (LRS) due to the “trap” effect. Guided by the simulation, a synapse cell consisted of a HfOX-based device and a fixed resistor series connection is proposed to achieve almost 100% recognition accuracy even if the resistance variation of LRS is 50%.