Evidence map›Paper›PMID 42579551›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

Coexisting Volatile and Nonvolatile Switching in 3D ALD-IGZO Vertical RRAM for Fully Hardware-Based Wide Reservoir Computing.

Seeun Lee, Jaewon Park, Nawoon Kim, Seohyeon Ju, Seong-Cheol Jang, Hyun-Suk Kim, Sungjun Kim

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

7 authors.

Seeun LeeDivision of Electronics and Electrical Engineering, Dongguk University, Seoul, Republic of Korea.
Jaewon ParkDepartment of Energy and Materials Engineering, Dongguk University, Seoul, Republic of Korea.ORCID https://orcid.org/0009-0005-1806-2634
Nawoon KimDivision of Electronics and Electrical Engineering, Dongguk University, Seoul, Republic of Korea.
Seohyeon JuDivision of Electronics and Electrical Engineering, Dongguk University, Seoul, Republic of Korea.
Seong-Cheol JangDepartment of Energy and Materials Engineering, Dongguk University, Seoul, Republic of Korea.
Hyun-Suk KimDepartment of Energy and Materials Engineering, Dongguk University, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0003-4286-7027
Sungjun KimDivision of Electronics and Electrical Engineering, Dongguk University, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0002-9873-2474

Funding

Ministry of Science and ICT RS-2025-25442614This research was supported by the ANCHOR through the Seoul ANCHOR Center, funded by the Ministry of Education (MOE) and the Seoul Metropolitan Government 2026-ANCHOR-01-007-04This work was supported by K-CHIPS (Korea Collaborative & High-tech Initiative for Prospective Semiconductor Research), the Ministry of Trade, Industry & Energy (MOTIE, Korea) 2410018408This work was supported by K-CHIPS (Korea Collaborative & High-tech Initiative for Prospective Semiconductor Research), the Ministry of Trade, Industry & Energy (MOTIE, Korea) 26087-45FCThis work was supported by K-CHIPS (Korea Collaborative & High-tech Initiative for Prospective Semiconductor Research), the Ministry of Trade, Industry & Energy (MOTIE, Korea) RS-2026-25529654
6 · The paper itself

Abstract

Although indium-gallium-zinc oxide (IGZO) has been widely used in thin-film transistors (TFTs), its application as an active switching medium in resistive random-access memory (RRAM) has remained relatively less explored, particularly for vertical RRAM (VRRAM) architectures. Here, we report an ALD-IGZO VRRAM platform in which the IGZO switching layer is conformally formed along vertical sidewalls, providing excellent step coverage and atomic-level compositional controllability for uniform and reliable switching. This platform realizes a compact dual-mode device, where the volatile switching mode before forming serves as the reservoir layer and the nonvolatile switching mode after forming functions as the readout layer. Leveraging these coexisting dynamics, we implement a fully hardware-based reservoir computing (RC) system with a wide architecture by operating multiple sub-reservoirs in parallel under distinct volatility conditions, achieving enhanced nonlinear mapping and feature separation. The proposed 2F ALD-IGZO VRRAM wide RC achieves 91.3% accuracy on the MNIST dataset with an estimated energy consumption of 3.70 nJ per classification, offering a scalable route toward learning-capable in-memory neuromorphic computing.

Indexed as

ALD‐IGZOin‐memory computingneuromorphic devicevertical resistive random‐access memorywide reservoir computing

Identifiers

PMID42579551
PMCPMC13460410

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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.