摘要：SummaryDriven by technologies such as machine learning, artificial intelligence, and internet of things, the energy efficiency and throughput limitations of the von Neumann architecture are becoming more and more serious. As a new type of computer architecture, computing-in-memory is an alternative approach to alleviate the von Neumann bottleneck. Here, we have demonstrated two kinds of computing-in-memory designs based on two-surface-channel MoS2transistors: symmetrical 4T2R Static Random-Access Memory (SRAM) cell and skewed 3T3R SRAM cell, where the symmetrical SRAM cell can realize in-memoryXNOR/XORcomputations and the skewed SRAM cell can achieve in-memoryNAND/NORcomputations. Furthermore, since both the memory and computing units are based on two-surface-channel transistors with high area efficiency, the two proposed computing-in-memory SRAM cells consume fewer transistors, suggesting a potential application in highly area-efficient and multifunctional computing chips.Graphical abstractDisplay OmittedHighlights•We demonstrate the symmetrical 4T2R and skewed 3T3R computing-in-memory SRAM•Both computing-in-memory SRAM cells consume six components, exhibiting higher area efficiency•The designed computing-in-memory SRAM cells support multiple Boolean logic operationsNanotechnology; Engineering; Devices
