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A Low-Power MRAM-Based Nonvolatile Flip-Flop Architecture for Register and System Applications

A Low-Power MRAM-Based Nonvolatile Flip-Flop Architecture for Register and System Applications 150 150
Author(s): Bayartulga Ishdorj, Taehui Na

Abstract:

This work presents a minimal nonvolatile flip-flop (NVFF) hybrid architecture and an NV static contention-free differential FF (nvSCDFF) cell that reduces the overhead associated with replicated NV circuitry in conventional in situ NVFFs while enabling low power. By using only one NVFF per column and compact SCDFF-based volatile flip-flops (FFs), …

Published in: IEEE Solid-State Circuits Letters
Page(s): 206 – 209
Year of Publication: 2026
Electronic ISSN: 2573-9603
DOI: 10.1109/LSSC.2026.3713204
Publisher: IEEE

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Topology-Aware Layout Design for Area-Decoupled Transistor Sizing in Multitier CFET SRAM

Topology-Aware Layout Design for Area-Decoupled Transistor Sizing in Multitier CFET SRAM 150 150
Author(s): Yutian Zhang, Jiaquan Fang, Khawar Sarfraz, Mansun Chan

Abstract:

This work shows that a multitier complementary FET (CFET) static random access memory (SRAM) can decouple the area term from PPA-oriented transistor sizing. A topology-aware layout design is used to construct orthogonal and point-symmetric multitier CFET SRAM cells under 1-nm-class design rules, enabling high-density (HD), high-performance (HP), and high-current (HC) …

Published in: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Page(s): 159 – 166
Year of Publication: 2026
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2026.3709007
Publisher: IEEE

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A Folded-Differential Switched-Capacitor SRAM CIM Macro With Scalable MAC Sizes for TinyML Inference

A Folded-Differential Switched-Capacitor SRAM CIM Macro With Scalable MAC Sizes for TinyML Inference 150 150
Author(s): Zhonghao Chen, Ling-An Cheong, Tianyi Yu, Yiming Chen, Guodong Yin, Teng Yi, Yongpan Liu, Huazhong Yang, Xueqing Li

Abstract:

This letter presents a switched-capacitor SRAM compute-in-memory macro optimized for TinyML inference. Key features include: 1) an area-efficient folded-differential multiply-and-accumulate (FD-MAC) scheme to double the signal margin; 2) a closed-loop floating-inverter amplifier (FIA)-based charge accumulation technique for signal-to-noise ratio enhancement and multiply-and-accumulate (MAC) voltage integration; and 3) a sparsity-aware multistep MAC method …

Published in: IEEE Solid-State Circuits Letters
Page(s): 73 – 76
Year of Publication: 2026
Electronic ISSN: 2573-9603
DOI: 10.1109/LSSC.2026.3662477
Publisher: IEEE

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AACIM: A 2785-TOPS/W, 161-TOP/mm2, <1.17%-RMSE, Analog-In Analog-Out Computing-In-Memory Macro in 28 nm

AACIM: A 2785-TOPS/W, 161-TOP/mm2, <1.17%-RMSE, Analog-In Analog-Out Computing-In-Memory Macro in 28 nm 150 150
Author(s): Rentao Wan, Chuan-Tung Lin, Bo Zhang, Yannis Tsividis, Mingoo Seok

Abstract:

This article presents an analog-in analog-out CIM macro (AACIM) for use in analog deep neural network (DNN) processors. Our macro receives analog inputs, performs a 64-by-32 vector–matrix multiplication (VMM) with a current-discharging computation mechanism, and produces analog outputs. It stores a 4-bit weight as an analog voltage in the …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 11
Year of Publication: 2025
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2025.3622309
Publisher: IEEE

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