Hybrid functional 3D artificial synapses for convolution and reinforcement learning.

Jiseong Im, Jangsaeng Kim, Jonghyun Ko, Wonjun Shin, Ryun-Han Koo, Jeonghyun Kim, Jong-Ho Lee

Abstract read

In one paragraph

Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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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

Who cites it

1 citing paper in PubMed.

Antimony-contact MoSMicrosystems & nanoengineering · 2026
Article

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.

Jiseong ImDepartment of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul 08826, Republic of Korea.ORCID 0000-0002-7260-9224

Jangsaeng KimDepartment of Electronic Engineering, Sogang University, Seoul 04107, Republic of Korea.ORCID 0000-0003-4519-135X

Jonghyun KoDepartment of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul 08826, Republic of Korea.ORCID 0000-0001-5641-2352

Wonjun ShinDepartment of Semiconductor Convergence Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea.ORCID 0000-0001-9122-2458

Ryun-Han KooDepartment of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul 08826, Republic of Korea.ORCID 0000-0002-5708-3308

Jeonghyun KimDepartment of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul 08826, Republic of Korea.

Jong-Ho LeeDepartment of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul 08826, Republic of Korea.ORCID 0000-0003-3559-9802

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent advancements in compute-in-memory (CIM) technology have garnered substantial interest, representing a promising shift in the computing paradigm. While conventional CIM systems using two-dimensional (2D) crossbar arrays have shown remarkable area and energy efficiency in fully connected neural networks, their applicability to convolutional neural networks remains inefficient. Herein, we introduce an architecture that enables convolution and reinforcement learning by leveraging the unique configuration of vertically stacked 3D flash memory. The proposed 3D convolution block (3D CB) significantly reduces area overhead and enhances the reliability and energy efficiency of convolution operations. The 3D CB and 3D fully connected block (3D FCB) exhibit distinct functionalities with minimal structural modifications. The compatibility between the 3D CB and the 3D FCB facilitates their seamless co-integration on a single wafer. The fully integrated system presented in this study demonstrates precise and consistent path planning capabilities for autonomous driving with high area and energy efficiency, paving the way for advanced CIM technology.

Identifiers

PMID41237232

DOI10.1126/sciadv.adw7498

PMCPMC13141905

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LicenceCC BY-NC

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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.