A flexible and fast digital twin for RRAM systems applied for training resilient neural networks.

ABSTRACT: Resistive Random Access Memory (RRAM) has gained considerable momentum due to its non-volatility and energy efficiency. Material and device scientists have been proposing novel material stacks that can mimic the "ideal memristor" which can deliver performance, energy efficiency, reliability and accuracy. However, designing RRAM-based systems is challenging. Engineering a new material stack, designing a device, and experimenting takes significant time for material and device researchers. Furthermore, the acceptability of the device is ultimately decided at the system level. We see a gap here where there is a need for facilitating material and device researchers with a "push button" modeling framework that allows to evaluate the efficacy of the device at system level during early device desi

SUBMITTER: Fritscher M 

PROVIDER: S-EPMC11467404 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature