Material to system-level benchmarking of CMOS-integrated RRAM with ultra-fast switching for low power on-chip learning.

ABSTRACT: Analog hardware-based training provides a promising solution to developing state-of-the-art power-hungry artificial intelligence models. Non-volatile memory hardware such as resistive random access memory (RRAM) has the potential to provide a low power alternative. The training accuracy of analog hardware depends on RRAM switching properties including the number of discrete conductance states and conductance variability. Furthermore, the overall power consumption of the system inversely correlates with the RRAM devices conductance. To study material dependence of these properties, TaOx and HfOx RRAM devices in one-transistor one-RRAM configuration (1T1R) were fabricated using a custom 65 nm CMOS fabrication process. Analog switching performance was studied with a range of initial forming c

SUBMITTER: Abedin M 

PROVIDER: S-EPMC10495451 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature