Yield and Reliability Optimization of Analog RRAM for In-Memory Computing on a 28nm CMOS Platform

RRAM is considered as one of the promising candidates for computing-in-memory (CIM) application. In pursuit of higher integration density, RRAM is gradually evolving to be integrated at increasingly advanced technology nodes. At the same time, RRAM also faces many new challenges. In this work, we improved the device yield by optimizing the material stacks and the device reliability by optimizing the operation schemes.

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Publication Details

Published
2025-03-09
DOI
https://doi.org/10.1109/edtm61175.2025.11040276
Citations
1
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.73

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article

Yield and Reliability Optimization of Analog RRAM for In-Memory Computing on a 28nm CMOS Platform

Bin Gao, Siyao Yang
1 citations
Advanced Memory and Neural Computing
0.73
article

Yield and Reliability Optimization of Analog RRAM for In-Memory Computing on a 28nm CMOS Platform

Bin Gao, Siyao Yang
article en
1 citations

Abstract

RRAM is considered as one of the promising candidates for computing-in-memory (CIM) application. In pursuit of higher integration density, RRAM is gradually evolving to be integrated at increasingly advanced technology nodes. At the same time, RRAM also faces many new challenges. In this work, we improved the device yield by optimizing the material stacks and the device reliability by optimizing the operation schemes.

Tsinghua University (CN)
National Science Foundation
Affordable and clean energy
Openalex Percentile: Top 27%
Advanced Memory and Neural Computing
0.73
DOISourceOpenAlex
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Yield and Reliability Optimization of Analog RRAM for In-Memory Computing on a 28nm CMOS Platform — Bin Gao, Siyao Yang · (2025) | TGRS Research Map | TGRS