The Impact of Thermal Enhance Layers on the Relaxation Effect in Analog RRAM

Computing-in-memory (CIM) with analog resistive random access memory (RRAM) has recently shown great potential in building energy-efficient hardware for artificial intelligence (AI). However, the relaxation effect of analog RRAM featuring post-programming conductance drift has become a key performance-limiting factor. In this work, a comprehensive study of the relaxation effect is presented from the analysis of its causes to the strategy for device optimization as well as the impact on CIM applications. An application-oriented quantitative indicator (relative deviation [RD]) is proposed to fairly evaluate the relaxation effect of different devices. In particular, the influence of oxygen content in different thermal enhanced layers (TELs) on the relaxation and maximum conductance value G(max) of analog RRAM is studied. A theory of ternary oxide TEL is proposed to mitigate relaxation while maintaining low G(max), which is experimentally validated by TaTiOx as TEL. Furthermore, neural network simulation is carried out to analyze the requirement for RRAM relaxation for CIM applications. This work provides a useful strategy for device optimization to suppress the relaxation effect by engineering the TEL.