Abstract
In-memory computing (IMC) is a technology that enables efficient analog vector-matrix multiplication (VMM). This field has been extensively researched to overcome the performance bottlenecks associated with traditional von Neumann architectures. In addition to analog VMM, combining efficient neuromorphic modules with memory is essential to enable a broader range of IMC operations. Here, we propose a complementary metal-oxide semiconductor (CMOS)-compatible flash-gated thyristor-based neuromorphic module (FGTNM) that combines various functions in neural networks, such as quantization, nonlinear activation, and max pooling into a single module. The FGTNM features a small footprint (53 square micrometers) and low energy consumption (9.1 femtojoules per operation), outperforming previous CMOS-based modules. System-level IMC using the FGTNM shows a high accuracy (89.97%) on CIFAR-10 classification. This work showcases the potential to co-integrate various devices (flash memory, flash-gated thyristor, n-type metal-oxide semiconductor, and p-type metal-oxide semiconductor) on a single wafer, which broadens the scope of IMC applications beyond analog VMM.