Vertical Self‐Rectifying Memristive Arrays for Page‐Wise Parallel Logic and Arithmetic Processing

Logic-in-memory (LIM) architectures are explored to address the data transfer bottleneck of conventional von Neumann architectures by integrating computation directly within memory arrays. Among various candidates, memristor-based LIM systems have gained significant attention due to their non-volatile switching behavior and compatibility with dense integration. In this work, a page-wise LIM architecture is implemented using a 3D vertical resistive random-access memory array composed of self-rectifying Pt-Ta<sub>2</sub>O<sub>5</sub>-Al:HfO<sub>2</sub>-TiN memristors. Two logic primitives-1 M and 2 M logic-are employed to perform intra-page and inter-page operations, respectively, enabling core Boolean functions to be executed entirely within the array through resistive state transitions. Based on these logic operations, a memristive arithmetic logic unit (mALU) is designed to perform essential arithmetic functions, including addition, subtraction, increment, and decrement. Owing to the vertical structure of vertical resistive random-access memory, a 2-bit full adder is implemented with a footprint of only three cells and completed in 12 steps. The proposed intra-and inter-page operations, along with complete mALU functionality, are experimentally demonstrated with high reproducibility. Combined with significantly reduced spatiotemporal cost, these results highlight the promise of this architecture for scalable and energy-efficient in-memory computing.

Authors

Institutions

Publication Details

Journal
Advanced Materials
Published
2025-11-24
DOI
https://doi.org/10.1002/adma.202514099
Citations
1
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.73

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Vertical Self‐Rectifying Memristive Arrays for Page‐Wise Parallel Logic and Arithmetic Processing

Soo Hyung Lee, Cheol Seong Hwang, Byeong‐Su Kim, Wonho Choi et al.
1 citations
Advanced Materials
Advanced Memory and Neural Computing
0.73
article

Vertical Self‐Rectifying Memristive Arrays for Page‐Wise Parallel Logic and Arithmetic Processing

Soo Hyung Lee, Cheol Seong Hwang, Byeong‐Su Kim, Wonho Choi, Yeong Rok Kim, Jung‐Kyu Lee, Dong Hoon Shin, N. Ghenzi, Janguk Han, Sungho Kim, Sunwoo Cheong, K.-A. Son, Jea Min Cho
article en
1 citations

Abstract

Logic-in-memory (LIM) architectures are explored to address the data transfer bottleneck of conventional von Neumann architectures by integrating computation directly within memory arrays. Among various candidates, memristor-based LIM systems have gained significant attention due to their non-volatile switching behavior and compatibility with dense integration. In this work, a page-wise LIM architecture is implemented using a 3D vertical resistive random-access memory array composed of self-rectifying Pt-Ta<sub>2</sub>O<sub>5</sub>-Al:HfO<sub>2</sub>-TiN memristors. Two logic primitives-1 M and 2 M logic-are employed to perform intra-page and inter-page operations, respectively, enabling core Boolean functions to be executed entirely within the array through resistive state transitions. Based on these logic operations, a memristive arithmetic logic unit (mALU) is designed to perform essential arithmetic functions, including addition, subtraction, increment, and decrement. Owing to the vertical structure of vertical resistive random-access memory, a 2-bit full adder is implemented with a footprint of only three cells and completed in 12 steps. The proposed intra-and inter-page operations, along with complete mALU functionality, are experimentally demonstrated with high reproducibility. Combined with significantly reduced spatiotemporal cost, these results highlight the promise of this architecture for scalable and energy-efficient in-memory computing.

Advanced MaterialsVol. 38(8)
Seoul National University (KR), Consejo Nacional de Investigaciones Científicas y Técnicas (AR), National University of Avellaneda (AR)
National Research Foundation, National Research Foundation of Korea
Openalex Percentile: Top 21%
Advanced Memory and Neural Computing
0.73
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.