Journal ArticleDOI
Alloying conducting channels for reliable neuromorphic computing
Han-Wool Yeon,Peng Lin,Chanyeol Choi,Scott H. Tan,Yongmo Park,Doyoon Lee,Jaeyong Lee,Feng Xu,Bin Gao,Huaqiang Wu,He Qian,Yifan Nie,Seyoung Kim,Seyoung Kim,Jeehwan Kim +14 more
TLDR
The discovery of an alloyed memristor with alloyed conduction channels enables stable and controllable device operation with high switching uniformity and allows the fabrication of large-scale crossbar arrays that feature a high device yield and accurate analogue programming capability.Abstract:
A memristor1 has been proposed as an artificial synapse for emerging neuromorphic computing applications2,3. To train a neural network in memristor arrays, changes in weight values in the form of device conductance should be distinct and uniform3. An electrochemical metallization (ECM) memory4,5, typically based on silicon (Si), has demonstrated a good analogue switching capability6,7 owing to the high mobility of metal ions in the Si switching medium8. However, the large stochasticity of the ion movement results in switching variability. Here we demonstrate a Si memristor with alloyed conduction channels that shows a stable and controllable device operation, which enables the large-scale implementation of crossbar arrays. The conduction channel is formed by conventional silver (Ag) as a primary mobile metal alloyed with silicidable copper (Cu) that stabilizes switching. In an optimal alloying ratio, Cu effectively regulates the Ag movement, which contributes to a substantial improvement in the spatial/temporal switching uniformity, a stable data retention over a large conductance range and a substantially enhanced programmed symmetry in analogue conductance states. This alloyed memristor allows the fabrication of large-scale crossbar arrays that feature a high device yield and accurate analogue programming capability. Thus, our discovery of an alloyed memristor is a key step paving the way beyond von Neumann computing.read more
Citations
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Journal ArticleDOI
2022 roadmap on neuromorphic computing and engineering
TL;DR: In this article , the authors present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of the neuromorphic computing community.
Journal ArticleDOI
Synaptic devices based neuromorphic computing applications in artificial intelligence
Bai Sun,Bai Sun,Tao Guo,Guangdong Zhou,Shubham Ranjan,Yixuan Jiao,Lan Wei,Y. Norman Zhou,Yimin A. Wu +8 more
TL;DR: In this article, the authors focus on the discussions of synaptic devices based neuromorphic computing applications in artificial intelligence and discuss future applications in neuromorphic vision, sensor, human machine intelligence, topological and quantum computing.
Posted Content
4K-Memristor Analog-Grade Passive Crossbar Circuit
TL;DR: This work reports a 64x64 passive metal-oxide memristor crossbar circuit with ~99% device yield, based on a foundry-compatible fabrication process featuring etch-down patterning and low-temperature budget, conducive to vertical integration.
Journal ArticleDOI
Filament-Free Bulk Resistive Memory Enables Deterministic Analogue Switching.
Yiyang Li,Elliot J. Fuller,Joshua D. Sugar,Sangmin Yoo,David S. Ashby,Christopher H. Bennett,Robert D. Horton,Michael S. Bartsch,Matthew J. Marinella,Wei Lu,A. Alec Talin +10 more
TL;DR: Bulk‐RRAM solves many outstanding issues with memristor unpredictability that have inhibited commercialization, and can, therefore, enable unprecedented new applications for energy‐efficient neuromorphic computing.
Journal ArticleDOI
4K-memristor analog-grade passive crossbar circuit.
TL;DR: Kim et al. as discussed by the authors reported a 64'×'64' passive crossbar circuit with ~99% functional nonvolatile metal-oxide memristors and achieved <26% coefficient of variance in memristor switching voltages.
References
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Journal ArticleDOI
The missing memristor found
TL;DR: It is shown, using a simple analytical example, that memristance arises naturally in nanoscale systems in which solid-state electronic and ionic transport are coupled under an external bias voltage.
Journal ArticleDOI
Nanoscale Memristor Device as Synapse in Neuromorphic Systems
TL;DR: A nanoscale silicon-based memristor device is experimentally demonstrated and it is shown that a hybrid system composed of complementary metal-oxide semiconductor neurons and Memristor synapses can support important synaptic functions such as spike timing dependent plasticity.
Journal ArticleDOI
Training and operation of an integrated neuromorphic network based on metal-oxide memristors
Mirko Prezioso,Farnood Merrikh-Bayat,Brian D. Hoskins,Gina C. Adam,Konstantin K. Likharev,Dmitri B. Strukov +5 more
TL;DR: The experimental implementation of transistor-free metal-oxide memristor crossbars, with device variability sufficiently low to allow operation of integrated neural networks, in a simple network: a single-layer perceptron (an algorithm for linear classification).
Journal ArticleDOI
Memristive crossbar arrays for brain-inspired computing
Qiangfei Xia,Jianhua Yang +1 more
TL;DR: The challenges in the integration and use in computation of large-scale memristive neural networks are discussed, both as accelerators for deep learning and as building blocks for spiking neural networks.
Journal ArticleDOI
Observation of conducting filament growth in nanoscale resistive memories
TL;DR: It is found that the filament growth can be dominated by cation transport in the dielectric film, and two different growth modes were observed for the first time in materials with different microstructures.