Evidence map›Paper›PMID 42720719›Full record

ArticleNano convergence2026

Capacitive in-memory vector-matrix multiplication computing with charge-trap memcapacitors.

Junsu Yu, Hwiho Hwang, Hyungjin Kim, Woo Young Choi

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nano convergence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Junsu YuDepartment of Electrical and Computer Engineering and Inter-University Semiconductor Research Center (ISRC), Seoul National University, Seoul, 08826, Republic of Korea.
Hwiho HwangDivision of Materials Science and Engineering, Department of Semiconductor Engineering, Hanyang University, Seoul, 04763, Republic of Korea.
Hyungjin KimDivision of Materials Science and Engineering, Department of Semiconductor Engineering, Hanyang University, Seoul, 04763, Republic of Korea. hkim12@hanyang.ac.kr.ORCID http://orcid.org/0000-0002-4834-6882
Woo Young ChoiDepartment of Electrical and Computer Engineering and Inter-University Semiconductor Research Center (ISRC), Seoul National University, Seoul, 08826, Republic of Korea. wooyoung@snu.ac.kr.

Funding

Institute for Information and Communications Technology Promotion RS-2021-II212052, RS-2024-00506767Korea Institute for Advancement of Technology RS-2024-00410066Ministry of Science and ICT, South Korea RS-2023-00260527Ministry of Science and ICT, South Korea RS-2025-02653611
6 · The paper itself

Abstract

Energy-efficient neural computing is increasingly limited not by computational throughput but by memory access and data movement. In-memory computing architectures offer a promising solution by collocating storage and computation, yet their practical realization remains constrained by static power dissipation, thermal challenges, and limited integration density in existing memory devices. Here, we demonstrate a vertically stackable charge-trap-flash (CTF)-based memcapacitor array that combines the high integration density of 3D NAND technology with the charge-domain computation. A continuous in-situ-doped N

Indexed as

3D NANDArtificial neural networksCharge trap flash (CTF)In-memory computing (IMC)Memcapacitors

Identifiers

PMID42720719
PMCPMC13562488

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.