2D materials-based 3D integration for neuromorphic hardware

Neuromorphic hardware enables energy-efficient computing, which is essential for a sustainable system. Recently, significant progress has been reported in neuromorphic hardware based on two-dimensional materials. However, traditional planar-integrated architectures still suffer from high energy consumption. This review systematically explores recent advances in the three-dimensional integration of two-dimensional material-based neuromorphic hardware to address these challenges. The materials, process, device physics, array, and integration levels are discussed, highlighting challenges and perspectives.

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Publication Details

Journal
npj 2D Materials and Applications
Published
2024-11-04
DOI
https://doi.org/10.1038/s41699-024-00509-1
Citations
38
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
7.93

Funders

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article

2D materials-based 3D integration for neuromorphic hardware

Hyeon-Ji Lee, Ho Won Jang, Chul‐Ho Lee, Seung Ju Kim
38 citations
npj 2D Materials and Applications
Advanced Memory and Neural Computing
7.93
article

2D materials-based 3D integration for neuromorphic hardware

Hyeon-Ji Lee, Ho Won Jang, Chul‐Ho Lee, Seung Ju Kim
article en
38 citations

Abstract

Neuromorphic hardware enables energy-efficient computing, which is essential for a sustainable system. Recently, significant progress has been reported in neuromorphic hardware based on two-dimensional materials. However, traditional planar-integrated architectures still suffer from high energy consumption. This review systematically explores recent advances in the three-dimensional integration of two-dimensional material-based neuromorphic hardware to address these challenges. The materials, process, device physics, array, and integration levels are discussed, highlighting challenges and perspectives.

npj 2D Materials and ApplicationsVol. 8(1)
University of Southern California (US), Seoul National University (KR), Advanced Institute of Convergence Technology (KR)
National Research Foundation of Korea
Openalex Percentile: Top 2%
Advanced Memory and Neural Computing
7.93
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