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Compute-in-memory (CIM) is a promising approach for efficiently performing data-centric computing (such as neural network computations). Among the multiple semiconductor memory technologies, embedded DRAM (eDRAM), which integrates the DRAM bit cell with high-performance logic transistors, can enable efficient CIM designs. However, the silicon-based eDRAM technology suffers from poor retention time-incurring significant refresh power overhead. However, eDRAM using back-end-of-line (BEOL) integrated <inline-formula> <tex-math notation="LaTeX">$C$ </tex-math></inline-formula>-axis aligned crystalline (CAAC) indium&#x2013;gallium&#x2013;zinc&#x2013;oxide (IGZO) transistors, exhibiting extreme low leakage, is a promising memory technology with lower refresh power overhead. A long retention time in IGZO eDRAM can enable multilevel cell functionality, which can improve its efficacy in CIM applications. In this article, we explore a capacitorless IGZO eDRAM-based multilevel cell, capable of storing 1.5 bits/cell for CIM designs focused on deep neural network (DNN) inference applications. We perform a detailed design space exploration of IGZO eDRAM sensitivity to process temperature variations for read, write, and retention operations followed by architecture-level simulations comparing performance and energy for different workloads. The effectiveness of IGZO eDRAM-based CIM architecture is evaluated using a representative neural network, and the proposed approach achieves 82&#x0025; Top-1 inference accuracy for the CIFAR-10 dataset, compared with 87&#x0025; software accuracy with high bit cell storage density.

IGZO CIM: Enabling In-Memory Computations Using Multilevel Capacitorless Indium–Gallium–Zinc–Oxide-Based Embedded DRAM Technology