AIMC Topic: Semiconductors

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An ultra-low-voltage electronic implementation of inertial neuron model with nonmonotonous Liao's activation function.

Network (Bristol, England) Mar 30, 2016
The output of every neuron in neural network is specified by the employed activation function (AF) and therefore forms the heart of neural networks. As far as the design of artificial neural networks (ANNs) is concerned, hardware approach is preferre...
Semiconductors Neural Networks, Computer Models, Neurological Electronics Neurons
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Evolutionary fuzzy ARTMAP neural networks for classification of semiconductor defects.

IEEE transactions on neural networks and learning systems Jul 1, 2014
Wafer defect detection using an intelligent system is an approach of quality improvement in semiconductor manufacturing that aims to enhance its process stability, increase production capacity, and improve yields. Occasionally, only few records that ...
Humans Neural Networks, Computer Artificial Intelligence Pattern Recognition, Automated Fuzzy Logic Semiconductors Algorithms Databases, Factual
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Experimental implementation of spike-based neuromorphic XOR operation based on polarization-mode competition in a single VCSOA.

Applied optics Jul 1, 2022
We experimentally and numerically propose an approach for implementing spike-based neuromorphic exclusive OR (XOR) operation using a single vertical-cavity semiconductor optical amplifier (VCSOA). XOR operation is realized based on the neuron-like in...
Optics and Photonics Photons Neural Networks, Computer Semiconductors
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Enhanced performance of a reservoir computing system based on a dual-loop optoelectronic oscillator.

Applied optics Apr 20, 2022
Time-delayed reservoir computing (RC) is a brain inspired paradigm for processing temporal information, with simplification in the network's architecture using virtual nodes embedded in a temporal delay line. In this work, a novel, to the best of our...
Computer Simulation Semiconductors Neural Networks, Computer
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RRAM-based synapse devices for neuromorphic systems.

Faraday discussions Feb 18, 2019
Hardware artificial neural network (ANN) systems with high density synapse array devices can perform massive parallel computing for pattern recognition with low power consumption. To implement a neuromorphic system with on-chip training capability, w...
Neural Networks, Computer Computers Hafnium Semiconductors Electronics Copper Electric Conductivity Equipment Design Oxides Aluminum Oxide
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Big data needs a hardware revolution.

Nature Feb 8, 2018
Brain Semiconductors Artificial Intelligence Computers Biomimetics Public-Private Sector Partnerships Humans
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Fully parallel write/read in resistive synaptic array for accelerating on-chip learning.

Nanotechnology Nov 13, 2015
A neuro-inspired computing paradigm beyond the von Neumann architecture is emerging and it generally takes advantage of massive parallelism and is aimed at complex tasks that involve intelligence and learning. The cross-point array architecture with ...
Electric Impedance Pattern Recognition, Automated Computing Methodologies Semiconductors Synapses Unsupervised Machine Learning Neural Networks, Computer Learning Models, Theoretical
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Training and operation of an integrated neuromorphic network based on metal-oxide memristors.

Nature May 7, 2015
Despite much progress in semiconductor integrated circuit technology, the extreme complexity of the human cerebral cortex, with its approximately 10(14) synapses, makes the hardware implementation of neuromorphic networks with a comparable number of ...
Engineering Synapses Transistors, Electronic Models, Neurological Biomimetics Oxides Algorithms Metals Humans Electronics Neural Networks, Computer Equipment Design Nanotechnology Semiconductors
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