Thermal Analysis and Evaluation of Memristor-based Compute-in-Memory Chips

The rapid advancement of artificial intelligence (AI) technologies has significantly increased the demand for high-performance computational hardware. Resistive Random-Access Memory (RRAM)-based Compute-in-Memory (CIM) technology shows great potential for addressing the data transfer bottleneck and supporting high-performance computing (HPC). In this paper, a multi-scale thermal model is developed to evaluate the temperature distribution in RRAM-based CIM chips and the influence of various factors on thermal behavior. The results indicate that hotspot temperatures can be mitigated by reducing the epoxy molding compound (EMC) thickness, increasing the substrate thickness, and lowering boundary thermal resistance. Moreover, optimizing the layout of analog computing circuits and digital circuits can reduce the maximum temperature by up to 4.04℃. Furthermore, the impact of temperature on the conductance of RRAM devices and the inference accuracy of RRAM-based CIM chips is analyzed. Simulation results reveal that thermal-induced accuracy loss in CIM chips is significant, but the computation correction method effectively reduces the accuracy loss from 66.4% to 1.4% at 85 ℃.