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Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

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Model-Inversion-Resistant Physical Unclonable Neural Network Using Vertical NAND Flash Memory.

Sung-Ho Park¹, Ryun-Han Koo¹, Jonghyun Ko¹

¹Department of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul, Republic of Korea.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)

|February 27, 2026

Summary

This summary is machine-generated.

We developed a secure neural network on flash memory, making it resistant to cloning and data theft. This physical unclonable neural network (PUNN) protects sensitive information in privacy-critical applications.

Keywords:

V‐NAND flash memory neural network security

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Area of Science:

Computer Science
Electrical Engineering
Cryptography

Background:

Growing use of neural networks in sensitive applications requires enhanced data and model protection.
Existing architectures are vulnerable to model-inversion and cloning attacks, compromising privacy and intellectual property.

Purpose of the Study:

To present a novel model-inversion-resistant physical unclonable neural network (PUNN) implemented on commercial vertical NAND (V-NAND) flash memory.
To demonstrate hardware-rooted security against model-cloning and model-inversion attacks while maintaining high accuracy.

Main Methods:

Implemented a PUNN using weak gate-induced drain-leakage erase on V-NAND flash memory to create unique, unreproducible device-level conductance patterns.
Utilized the forward-forward (FF) algorithm for training, compatible with V-NAND's common-source-line structure, eliminating backward propagation.
Evaluated model non-clonability by transferring trained weights between chips and assessed privacy preservation on the MIT-BIH electrocardiogram dataset.

Main Results:

The V-NAND FF-PUNN demonstrated hardware-rooted resistance to model-cloning, with inference accuracy collapsing upon weight transfer to different chips due to inherent randomness.
The system achieved high accuracy in classifying electrocardiogram data while completely preventing data reconstruction via model-inversion attacks.
The PUNN maintained competitive classification accuracy under forward-only learning.

Conclusions:

Established a scalable framework for secure, energy-efficient, and privacy-preserving neural computing directly on commercial flash memory.
The V-NAND FF-PUNN offers a practical solution for protecting sensitive data and model integrity in real-world applications.
This approach provides intrinsic non-clonability and robust defense against sophisticated cyber threats.