Thermally Pre‐Formed Reconfigurable Resistive Random‐Access Memory Crossbar Arrays: A Dual‐Mode Platform for Robust Physically Unclonable Functions and In‐Memory Computing

ABSTRACT Intelligent edge hardware requires both high‐entropy physical signatures for security and highly uniform devices for accurate In‐Memory Computing (IMC), yet conventional Resistive Random Access Memory (RRAM) relies on electroforming that irreversibly damages the dielectric and fixes the filament state. A forming‐free RRAM platform is demonstrated in which Thermally Pre‐formed Filaments (TPFs) are generated by solid‐state oxygen scavenging between an oxygen‐deficient TiO x reservoir and an ultrathin Al 2 O 3 switching layer. Thermodynamically driven, reversible ionic migration forms percolation‐threshold conduction networks that produce a device‐unique distribution of initial resistance states, enabling Physically Unclonable Functions (PUFs). A reverse‐bias reset fully erases the conductive network and restores a pristine‐like high‐resistance state, allowing the same array to be reconfigured into a uniform analog synaptic platform without additional forming. In a 24 × 24 crossbar array, the PUF responses pass the National Institute of Standards and Technology (NIST) SP 800–22 test suite, and the reconfigured array shows high‐fidelity multilevel conductance programming for neuromorphic IMC. This reversible transition from stochastic security primitives to uniform analog computing establishes a damage‐free route toward reconfigurable secure Artificial Intelligence (AI) hardware.

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Publication Details

Journal
Advanced Functional Materials
Published
2026-06-17
DOI
https://doi.org/10.1002/adfm.76481
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Thermally Pre‐Formed Reconfigurable Resistive Random‐Access Memory Crossbar Arrays: A Dual‐Mode Platform for Robust Physically Unclonable Functions and In‐Memory Computing

D W Kim, Taewon Lee, Sungjoon Kim, Seongbin Kwon et al.
Advanced Functional Materials
Advanced Memory and Neural Computing
article

Thermally Pre‐Formed Reconfigurable Resistive Random‐Access Memory Crossbar Arrays: A Dual‐Mode Platform for Robust Physically Unclonable Functions and In‐Memory Computing

D W Kim, Taewon Lee, Sungjoon Kim, Seongbin Kwon, Sungmin Hwang
article en

Abstract

ABSTRACT Intelligent edge hardware requires both high‐entropy physical signatures for security and highly uniform devices for accurate In‐Memory Computing (IMC), yet conventional Resistive Random Access Memory (RRAM) relies on electroforming that irreversibly damages the dielectric and fixes the filament state. A forming‐free RRAM platform is demonstrated in which Thermally Pre‐formed Filaments (TPFs) are generated by solid‐state oxygen scavenging between an oxygen‐deficient TiO x reservoir and an ultrathin Al 2 O 3 switching layer. Thermodynamically driven, reversible ionic migration forms percolation‐threshold conduction networks that produce a device‐unique distribution of initial resistance states, enabling Physically Unclonable Functions (PUFs). A reverse‐bias reset fully erases the conductive network and restores a pristine‐like high‐resistance state, allowing the same array to be reconfigured into a uniform analog synaptic platform without additional forming. In a 24 × 24 crossbar array, the PUF responses pass the National Institute of Standards and Technology (NIST) SP 800–22 test suite, and the reconfigured array shows high‐fidelity multilevel conductance programming for neuromorphic IMC. This reversible transition from stochastic security primitives to uniform analog computing establishes a damage‐free route toward reconfigurable secure Artificial Intelligence (AI) hardware.

Advanced Functional Materials
Sejong University (KR), Korea Labor Institute (KR)
National Research Foundation of Korea
Reduced inequalities
Openalex Percentile: Top 11%
Advanced Memory and Neural Computing
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