CMOS‐Compatible LiNbO <sub>3</sub> Domain‐Wall Entropy Engine for Ultrafast True Random Number Generation

Random ferroelectric domain nucleation and growth lead to the generation of numerous unpredictable microscopic states that collectively form a natural high-entropy system. Conventional electrical methods can directly measure reversible domain switching currents, offering a viable platform for true random number generation (TRNG). However, TRNG based on random ferroelectric switching events is limited by the noise amplitudes in electrical signals. In this study, TRNG is realized via the stochastic formation of conductive domain walls in single-crystal LiNbO<sub>3</sub> thin films bonded to SiO<sub>2</sub>/Si wafers. This approach achieves a noise amplitude and cycling endurance >500 nA and >10<sup>10</sup>, respectively. The interfacial-layer-based device exhibits self-reinitialized stochastic sub-10-ns domain switching operations, enabling ultrafast generation of bit outputs and flexible device scaling. The generated random bitstreams, validated via National Institute of Standards and Technology (NIST)tests, exhibit robust resistance against machine-learning-based predictive attacks. This pioneering study establishes ferroelectric conductive domain walls as groundbreaking platforms for CMOS-compatible entropy source extraction, effectively addressing the long-standing challenges in amplifying entropy signals with operational robustness.

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Publication Details

Journal
Advanced Materials
Published
2025-10-11
DOI
https://doi.org/10.1002/adma.202510981
Primary Topic
Advanced Memory and Neural Computing
Type
article
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article

CMOS‐Compatible LiNbO 3 Domain‐Wall Entropy Engine for Ultrafast True Random Number Generation

Di Hu, Haiyue Tang, Qianwei Huang, Wen Di Zhang et al.
Advanced Materials
Advanced Memory and Neural Computing
article

CMOS‐Compatible LiNbO 3 Domain‐Wall Entropy Engine for Ultrafast True Random Number Generation

Di Hu, Haiyue Tang, Qianwei Huang, Wen Di Zhang, Zilong Wang, Xinglong Wang, Xianyu Hu, Anquan Jiang
article en

Abstract

Random ferroelectric domain nucleation and growth lead to the generation of numerous unpredictable microscopic states that collectively form a natural high-entropy system. Conventional electrical methods can directly measure reversible domain switching currents, offering a viable platform for true random number generation (TRNG). However, TRNG based on random ferroelectric switching events is limited by the noise amplitudes in electrical signals. In this study, TRNG is realized via the stochastic formation of conductive domain walls in single-crystal LiNbO<sub>3</sub> thin films bonded to SiO<sub>2</sub>/Si wafers. This approach achieves a noise amplitude and cycling endurance >500 nA and >10<sup>10</sup>, respectively. The interfacial-layer-based device exhibits self-reinitialized stochastic sub-10-ns domain switching operations, enabling ultrafast generation of bit outputs and flexible device scaling. The generated random bitstreams, validated via National Institute of Standards and Technology (NIST)tests, exhibit robust resistance against machine-learning-based predictive attacks. This pioneering study establishes ferroelectric conductive domain walls as groundbreaking platforms for CMOS-compatible entropy source extraction, effectively addressing the long-standing challenges in amplifying entropy signals with operational robustness.

Advanced MaterialsVol. 38(4)
University of Shanghai for Science and Technology (CN), Shanghai Innovative Research Center of Traditional Chinese Medicine (CN)
Openalex Percentile: Top 72%
Advanced Memory and Neural Computing
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CMOS‐Compatible LiNbO <sub>3</sub> Domain‐Wall Entropy Engine for Ultrafast True Random Number Generation — Di Hu, Haiyue Tang, et al. · Advanced Materials (2025) | TGRS Research Map | TGRS