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 TaTiO _x 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.