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

K Kunhee Son J Jea Min Cho (Department of Materials Science and Engineering and Inter‐university Semiconductor Research Center, College of Engineering Seoul National University Seoul 08826 Republic of Korea) D Dong Hoon Shin Y Yeong Rok Kim (Department of Materials Science and Engineering and Inter‐university Semiconductor Research Center, College of Engineering Seoul National University Seoul 08826 Republic of Korea) N Néstor Ghenzi S Sunwoo Cheong B Byeong Su Kim J Jung Kyu Lee (Department of Materials Science and Engineering and Inter‐university Semiconductor Research Center, College of Engineering Seoul National University Seoul 08826 Republic of Korea) S Sungho Kim W Wonho Choi S Soo Hyung Lee J Janguk Han C Cheol Seong Hwang

Abstract

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 2 O 5 –Al:HfO 2 –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.

Article Details

Volume / Issue Vol. 38, Issue 8
Published February 01, 2026
ISSN 0935-9648
Publisher Unknown Publisher

Journal Info

Advanced Materials

Unknown Publisher

ISSN: 0935-9648 Physical Sciences

Authors (13)

K

Kunhee Son

J

Jea Min Cho

Department of Materials Science and Engineering and Inter‐university Semiconductor Research Center, College of Engineering Seoul National University Seoul 08826 Republic of Korea

D

Dong Hoon Shin

Y

Yeong Rok Kim

Department of Materials Science and Engineering and Inter‐university Semiconductor Research Center, College of Engineering Seoul National University Seoul 08826 Republic of Korea

N

Néstor Ghenzi

S

Sunwoo Cheong

B

Byeong Su Kim

J

Jung Kyu Lee

Department of Materials Science and Engineering and Inter‐university Semiconductor Research Center, College of Engineering Seoul National University Seoul 08826 Republic of Korea

S

Sungho Kim

W

Wonho Choi

S

Soo Hyung Lee

J

Janguk Han

C

Cheol Seong Hwang