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Characterization and Modeling of Multilevel Analog ReRAM Synapses in the Sky130 Process

Characterization and Modeling of Multilevel Analog ReRAM Synapses in the Sky130 Process 150 150
Author(s): Irem Didin, Carl Brando, Ching-Yi Lin, Sahil Shah

Abstract:

Nonvolatile memory devices play a key role in enabling energy-efficient computing. Among them, analog nonvolatile memories such as resistive random access memory (ReRAM) offer high density and low power compared to conventional digital memories. However, their analog nature introduces device-level variability that impacts computational accuracy. This work presents the characterization …

Published in: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Page(s): 27 – 35
Year of Publication: 2026
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2026.3670667
Publisher: IEEE

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On-Chip Charge-Trap-Transistor-Based Mismatch Calibration of an 8-Bit Thermometer Current-Source DAC

On-Chip Charge-Trap-Transistor-Based Mismatch Calibration of an 8-Bit Thermometer Current-Source DAC 150 150
Author(s): Mohammadreza Zeinali, Sudhakar Pamarti

Abstract:

This letter presents an on-chip mismatch calibration technique for current-source digital-to-analog converters (DACs) using charge-trap transistors (CTTs) in 22-nm FDSOI technology. The proposed method exploits programmable threshold voltage (VTH) shifts in CTTs to locally tune the current of near-minimum-sized devices without external trimming. A compact 8-bit thermometer DAC is implemented …

Published in: IEEE Solid-State Circuits Letters
Page(s): 53 – 56
Year of Publication: 2026
Electronic ISSN: 2573-9603
DOI: 10.1109/LSSC.2026.3656261
Publisher: IEEE

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A 28-nm FeFET Compute-in-Memory Macro With 64×64 Array Size and On-Chip 4-Bit Flash ADC

A 28-nm FeFET Compute-in-Memory Macro With 64×64 Array Size and On-Chip 4-Bit Flash ADC 150 150
Author(s): Vaidehi Garg, Jianwei Jia, Omkar Phadke, Shimeng Yu

Abstract:

Compute-in-memory (CIM) using emerging nonvolatile memory devices is a promising candidate for energy-efficient deep neural network (DNN) inference at the edge. Ferroelectric field-effect transistors (FeFETs) have recently gained attention as nonvolatile, CMOS-compatible devices with a higher on/off ratio and lower read and write energy compared to resistive random-access memory (…

Published in: IEEE Solid-State Circuits Letters
Page(s): 13 – 16
Year of Publication: 2026
Electronic ISSN: 2573-9603
DOI: 10.1109/LSSC.2025.3640522
Publisher: IEEE

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