The escalating need for high-performance artificial intelligence(AI)computing intensifies the"memory bottleneck"of the von Neumann architecture,prompting extensive exploration of computation-in-memory(CIM)so...The escalating need for high-performance artificial intelligence(AI)computing intensifies the"memory bottleneck"of the von Neumann architecture,prompting extensive exploration of computation-in-memory(CIM)solutions.This study is cen-tered on the optimization of a high-efficiency,low-power"L"-shaped split-gate floating-gate(FG)memory for CIM applications.Fabricated on a 55 nm CMOS platform,the memory devices were systematically investigated through wafer acceptance test(WAT),Sentaurus™simulations and comprehensive evaluations with the DNN+NeuroSim Framework V2.0.Among devices with diverse FG lengths,the 95-nm FG variant exhibits outstanding performance:it achieves a 5.35 V memory window,reaches a maximum conductance of 16.7μS with excellent linearity under the varying voltage and width pulse scheme(VWPS),real-izes 32-state multi-level storage,and attains a 92%training accuracy on the CIFAR-10 dataset using the VGG8 neural network.展开更多
基金supported by National Key Research and Development Program of China(2022YFF0605803)Zhejiang key R&D project(2023C01017)the Zhejiang Key Research and Development Project(2024SJCZX0030).
文摘The escalating need for high-performance artificial intelligence(AI)computing intensifies the"memory bottleneck"of the von Neumann architecture,prompting extensive exploration of computation-in-memory(CIM)solutions.This study is cen-tered on the optimization of a high-efficiency,low-power"L"-shaped split-gate floating-gate(FG)memory for CIM applications.Fabricated on a 55 nm CMOS platform,the memory devices were systematically investigated through wafer acceptance test(WAT),Sentaurus™simulations and comprehensive evaluations with the DNN+NeuroSim Framework V2.0.Among devices with diverse FG lengths,the 95-nm FG variant exhibits outstanding performance:it achieves a 5.35 V memory window,reaches a maximum conductance of 16.7μS with excellent linearity under the varying voltage and width pulse scheme(VWPS),real-izes 32-state multi-level storage,and attains a 92%training accuracy on the CIFAR-10 dataset using the VGG8 neural network.
