An artificial synapse by superlattice-like phase-change material for low-power brain-inspired computing
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Summary
A GeTe/Sb2Te3 superlattice-like PCM device which allows a progressive RESET process and is implemented in both artificial neural networks (ANN) and convolutional neural network (CNN) simulations, demonstrating high accuracy in brain-like pattern recognition.
- Type
- article
- Published
- 2020-06-01
- Cited by
- 4
- References
- 49
- OpenAlex
- https://openalex.org/W3016575180
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:218822927
Keywords
Computer science, Neuromorphic engineering, Reset (finance), Superlattice, Memristor
References
- Interface formation of two- and three-dimensionally bonded materials in the case of GeTe-Sb₂Te₃ superlattices.
- Experimental demonstration and tolerancing of a large-scale neural network (165,000 synapses), using phase-change memory as the synaptic weight element
- Emulation of spike-timing dependent plasticity in nano-scale phase change memory
- Visual Pattern Extraction Using Energy-Efficient “2-PCM Synapse” Neuromorphic Architecture
- Overview of candidate device technologies for storage-class memory
- Nanoscale memristor device as synapse in neuromorphic systems.
- Memristive devices for computing.
- Thermal conductivity of chalcogenide material with superlatticelike structure
- Role of vacancies in metal-insulator transitions of crystalline phase-change materials.
- Low-Energy Robust Neuromorphic Computation Using Synaptic Devices
- Crystal growth within a phase change memory cell
- Interfacial phase-change memory.
- A million spiking-neuron integrated circuit with a scalable communication network and interface
- Multi-column deep neural networks for image classification
- Best practices for convolutional neural networks applied to visual document analysis
- ImageNet classification with deep convolutional neural networks
- Large-scale neural networks implemented with non-volatile memory as the synaptic weight element: Comparative performance analysis (accuracy, speed, and power)
- Phase‐Change Memory Materials by Design: A Strain Engineering Approach
- EIE: Efficient Inference Engine on Compressed Deep Neural Network
- Understanding phase-change behaviors of carbon-doped Ge₂Sb₂Te₅ for phase-change memory application.
Cited by
- Universal memory based on phase-change materials: From phase-change random access memory to optoelectronic hybrid storage*
- Phase transition behavior and electronic properties of GaSb/Ge2Sb2Te5 superlattice-like structure thin films
- Set/Reset Bilaterally Controllable Resistance Switching Ga‐doped Ge2Sb2Te5 Long‐Term Electronic Synapses for Neuromorphic Computing
- Growth and microstructure of GeTe-Sb2Te3 heterostructures prepared by pulsed laser deposition
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