ArticleScientific reports2022
Multimodal transistors as ReLU activation functions in physical neural network classifiers.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- Reconfigurable ferroelectric transistor array for embodied neuromorphic vision with hardware-native sensing, computing, and activation.Science advances · 2026Article
- A reconfigurable photosensitive split-floating-gate memory for neuromorphic computing and nonlinear activation.Nature communications · 2026Article
- Neuron signal attenuation activation mechanism for deep learning.Patterns (New York, N.Y.) · 2025Article
- Split-Gate: Harnessing Gate Modulation Power in Thin-Film Electronics.Micromachines · 2024Review
- An intelligent diabetes classification and perception framework based on ensemble and deep learning method.PeerJ. Computer science · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Artificial neural networks (ANNs) providing sophisticated, power-efficient classification are finding their way into thin-film electronics. Thin-film technologies require robust, layout-efficient devices with facile manufacturability. Here, we show how the multimodal transistor's (MMT's) transfer characteristic, with linear dependence in saturation, replicates the rectified linear unit (ReLU) activation function of convolutional ANNs (CNNs). Using MATLAB, we evaluate CNN performance using systematically distorted ReLU functions, then substitute measured and simulated MMT transfer characteristics as proxies for ReLU. High classification accuracy is maintained, despite large variations in geometrical and electrical parameters, as CNNs use the same activation functions for training and classification.
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Registered trials
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