Exploring the Potential of a 1T-1M HfOX-Based Resistive Switching Device for Articulating Artificial Neural Network Hardware

In this work, we investigate the pattern recognition accuracy and energy consumption of crossbar architectures based on HfOX-based one-transistor one-memristor (1T-1M) cells, utilizing a fully calibrated analytical model. Our results highlight the uniform and well separated low resistance states (LRS) with HfOX-based 1T-1M cells provide excellent noise-immune operations with a recognition accuracy of approximately 47% in the presence of a 26% noise level. Further, the HfOX-based crossbar array maintains an average energy consumption of around 18 nJ during data storage within each cell. Finally, the artificial neural network designed with 1T-1M demonstrates the 90% recognition capabilities in handwritten MNIST digits.