A Robust Computing-in-Memory Macro With 2T1R1C Cells and Reused Capacitors for Successive-Approximation ADC

Abstract

Computing-in-memory (CIM) has emerged as a practical paradigm to bypass the von Neumann bottleneck. However, traditional CIM schemes face challenges due to the nonideal characteristics of nonvolatile memory (NVM). To address this issue, this work provides a resistive random access memory (RRAM)-based CIM macro employing two-transistor-one-RRAM–one-capacitor (2T1R1C) cells, with capacitors reused for the successive-approximation analog-to-digital converter (SAR ADC). Single-level RRAM is utilized to mitigate resistance variation. The multiply-accumulate (MAC) operation is performed via the charge and discharge of capacitors, enhancing robustness across different process, voltage, and temperature (PVT) corners. The capacitors in 2T1R1C cells are repurposed as sampling capacitors to integrate the ADC with the array. A precision-adjustable SAR (PA-SAR) logic is proposed to generate partial sums at varying precision levels aligned with different input bits, optimizing energy efficiency while maintaining reliability. Our proposed 2T1R1C array features an average area of $3.403~\mu $ m2 for each cell, which accounts for 87.46% of the total macro area. The total macro area is 1.020 mm2 with a capacity of 256 Kb, achieving an energy density of 0.201 TOPS/mm2. The PA-SAR logic boosts energy efficiency to 44.71 TOPS/W, marking a 38.55% improvement over conventional full-precision schemes.