Evidence map›Paper›PMID 38520711›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2024

A Self-Oscillated Organic Synapse for In-Memory Two-Factor Authentication.

Shuzhi Liu, Xiaolong Zhong, Yuxuan Li, Bingjie Guo, Zhilong He, Zhixin Wu, Sixian Liu, Yanbo Guo, Xiaoling Shi, Weilin Chen and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. A Self-Oscillated Organic Synapse for In-Memory Two-Factor Authentication.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Shuzhi LiuDepartment of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.ORCID 0009-0006-8033-3017
Xiaolong ZhongDepartment of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Yuxuan LiSchool of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Bingjie GuoSchool of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Zhilong HeSchool of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Zhixin WuDepartment of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Sixian LiuDepartment of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Yanbo GuoDepartment of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Xiaoling ShiDepartment of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Weilin ChenDepartment of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Hongxiao DuanDepartment of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Jianmin ZengDepartment of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Gang LiuDepartment of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.ORCID 0000-0002-3142-9323

Funding

National Key Research and Development Program of China 2022YFB4700102
6 · The paper itself

Abstract

Entering the era of AI 2.0, bio-inspired target recognition facilitates life. However, target recognition may suffer from some risks when the target is hijacked. Therefore, it is significantly important to provide an encryption process prior to neuromorphic computing. In this work, enlightened from time-varied synaptic rule, an in-memory asymmetric encryption as pre-authentication is utilized with subsequent convolutional neural network (ConvNet) for target recognition, achieving in-memory two-factor authentication (IM-2FA). The unipolar self-oscillated synaptic behavior is adopted to function as in-memory asymmetric encryption, which can greatly decrease the complexity of the peripheral circuit compared to bipolar stimulation. Results show that without passing the encryption process with suitable weights at the correct time, the ConvNet for target recognition will not work properly with an extremely low accuracy lower than 0.86%, thus effectively blocking out the potential risks of involuntary access. When a set of correct weights is evolved at a suitable time, a recognition rate as high as 99.82% can be implemented for target recognition, which verifies the effectiveness of the IM-2FA strategy.

Indexed as

Neural Networks, ComputerSynapsesAlgorithmsHumansin‐memory asymmetric encryptionin‐memory two‐factor authenticationorganic synapseself‐oscillated characteristic

Identifiers

PMID38520711
PMCPMC11151044

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.