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Related Experiment Video

Updated: Jan 15, 2026

Rapid Repetition Rate Fluctuation Measurement of Soliton Crystals in a Microresonator

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Published on: December 15, 2021

CMOS-Compatible LiNbO₃ Domain-Wall Entropy Engine for Ultrafast True Random Number Generation.

Haiyue Tang¹, Zilong Wang¹, Xinglong Wang¹

¹College of Integrated Circuits and Micro-Nano Electronics Innovation, Fudan University, Shanghai, 200433, China.

Advanced Materials (Deerfield Beach, Fla.)

|October 11, 2025

Summary

This summary is machine-generated.

This study introduces a new method for true random number generation (TRNG) using ferroelectric conductive domain walls in lithium niobate thin films. This approach overcomes limitations of traditional methods, offering faster and more robust random bit generation.

Keywords:

domain wall ferroelectric domain switching hardware security lithium niobate true‐random‐number generator

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Area of Science:

Materials Science
Condensed Matter Physics
Semiconductor Devices

Background:

Ferroelectric materials exhibit random domain nucleation and growth, forming high-entropy systems.
Conventional true random number generation (TRNG) using electrical methods faces limitations due to low noise amplitudes.
Existing TRNG methods struggle with signal amplification and operational robustness.

Purpose of the Study:

To develop a novel TRNG platform utilizing stochastic conductive domain walls in ferroelectric thin films.
To overcome the noise amplitude limitations of conventional electrical TRNG methods.
To establish a robust and scalable entropy source for secure random number generation.

Main Methods:

Fabrication of single-crystal LiNbO3 thin films bonded to SiO2/Si wafers.
Utilizing stochastic formation and switching of conductive domain walls as the entropy source.
Characterization of noise amplitude, cycling endurance, and switching speeds of the domain wall device.

Main Results:

Achieved high noise amplitude (>500 nA) and exceptional cycling endurance (>10^10).
Demonstrated ultrafast, self-reinitialized stochastic domain switching operations (<10 ns).
Generated random bitstreams validated by NIST tests, showing resistance to machine learning attacks.

Conclusions:

Ferroelectric conductive domain walls provide a groundbreaking platform for CMOS-compatible entropy source extraction.
This approach effectively addresses challenges in amplifying entropy signals with operational robustness.
The developed TRNG method offers a promising solution for ultrafast and secure random number generation.