Simulation of synaptic properties of ferroelectric memory capacitors and neural network applications

Abstract In this work, the electrical properties and synaptic characteristics of hafnium oxide-based ferroelectric memory capacitor with metal - ferroelectric layer - metal (MFM) structure were simulated using TCAD (technology computer aided design) software. Based on the synaptic potentiation/depression characteristics of the simulated memory capacitor, a multilayer perceptron (MLP) network was constructed, and the recognition accuracy and convergence speed of the MLP network in the MNIST recognition task were simulated, and the feasibility of the ferroelectric memory capacitor synaptic device for real neural network operation was analyzed. The results show that the recognition accuracy of the MLP network reaches 93% and stabilizes after 50 iterations of training, and the recognition accuracy of the MLP network is already at a high usable level after a smaller number of training times of 20, which suggests that the synaptic plasticity of the ferroelectric memory capacitor has a good potential for the practical application of the weight updating of the MLP network.