Electrolyte-gated transistors with good retention for neuromorphic computing

2022 ◽  
Vol 120 (2) ◽  
pp. 021901
Author(s):  
Yue Li ◽  
Han Xu ◽  
Jikai Lu ◽  
Zuheng Wu ◽  
Shuyu Wu ◽  
...  
Keyword(s):  
Neuromorphic Computing ◽  
Good Retention

LESCO T-15 PM

Alloy Digest ◽  
1990 ◽  
Vol 39 (8) ◽  
Keyword(s):  
Powder Metallurgy ◽  
Physical Properties ◽  
Tool Steel ◽  
Abrasion Resistance ◽  
High Speed ◽  
Heat Treating ◽  
High Carbon ◽  
Steel Company ◽  
Good Retention ◽  
Hot Hardness

Abstract LESCOT-15 PM is a special purpose tungsten type high-speed tool steel containing cobalt for good retention of hot hardness and high carbon and vanadium for enhanced abrasion resistance. It is produced by powder metallurgy. This datasheet provides information on composition, physical properties, microstructure, hardness, and elasticity. It also includes information on heat treating and machining. Filing Code: TS-498. Producer or source: Latrobe Steel Company. Originally published August 1989, revised August 1990.

Foundations of Neuromorphic Computing

2013 ◽  
Author(s):  
Clare Thiem ◽  
Bryant Wysocki ◽  
Morgan Bishop ◽  
Nathan McDonald ◽  
James Bohl
Keyword(s):  
Neuromorphic Computing

Neuromorphic Computing for Very Large Test and Evaluation Data Analysis

2014 ◽  
Author(s):  
Bryant Wysocki ◽  
Nathan McDonald ◽  
Clare Thiem ◽  
Thomas Renz ◽  
James Bohl
Keyword(s):  
Data Analysis ◽  
Neuromorphic Computing ◽  
Evaluation Data ◽  
Test And Evaluation

Embracing the era of neuromorphic computing

2021 ◽  
Vol 42 (1) ◽  
pp. 010301
Author(s):  
Yanghao Wang ◽  
Yuchao Yang ◽  
Yue Hao ◽  
Ru Huang
Keyword(s):  
Neuromorphic Computing

Large-Scale and Flexible Optical Synapses for Neuromorphic Computing and Integrated Visible Information Sensing Memory Processing

ACS Nano ◽  
2020 ◽  
Author(s):  
Ya-Xin Hou ◽  
Yi Li ◽  
Zhi-Cheng Zhang ◽  
Jia-Qiang Li ◽  
De-Han Qi ◽  
...  
Keyword(s):  
Large Scale ◽  
Neuromorphic Computing ◽  
Memory Processing

scholarly journals Flexible and Transparent Memristive Synapse Based on Polyvinylpyrrolidone/N-doped Carbon Quantum Dot Nanocomposites for Neuromorphic Computing

2021 ◽  
Author(s):  
Tao Zeng ◽  
Zhi Yang ◽  
Jiabing Liang ◽  
Ya Lin ◽  
Yankun Cheng ◽  
...  
Keyword(s):  
Quantum Dot ◽  
Neuromorphic Computing ◽  
Carbon Quantum Dot ◽  
Hardware Implementations

Memristive devices are widely recognized as promising hardware implementations of neuromorphic computing. Herein, a flexible and transparent memristive synapse based on polyvinylpyrrolidone (PVP)/N-doped carbon quantum dot (NCQD) nanocomposites through regulating...

Synaptic devices based neuromorphic computing applications in artificial intelligence

2021 ◽  
pp. 100393
Author(s):  
Bai Sun ◽  
Tao Guo ◽  
Guangdong Zhou ◽  
Shubham Ranjan ◽  
Yixuan Jiao ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neuromorphic Computing

Spin–Orbit Torque‐Induced Domain Nucleation for Neuromorphic Computing

2021 ◽  
pp. 2103672
Author(s):  
Jing Zhou ◽  
Tieyang Zhao ◽  
Xinyu Shu ◽  
Liang Liu ◽  
Weinan Lin ◽  
...  
Keyword(s):  
Spin Orbit ◽  
Neuromorphic Computing

scholarly journals Graphene-based 3D XNOR-VRRAM with ternary precision for neuromorphic computing

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Batyrbek Alimkhanuly ◽  
Joon Sohn ◽  
Ik-Joon Chang ◽  
Seunghyun Lee
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Recognition Accuracy ◽  
Material Selection ◽  
Weighted Sum ◽  
Device Design ◽  
Key Factors ◽  
Neuromorphic Computing ◽  
Device Scaling ◽  
The Impact

AbstractRecent studies on neural network quantization have demonstrated a beneficial compromise between accuracy, computation rate, and architecture size. Implementing a 3D Vertical RRAM (VRRAM) array accompanied by device scaling may further improve such networks’ density and energy consumption. Individual device design, optimized interconnects, and careful material selection are key factors determining the overall computation performance. In this work, the impact of replacing conventional devices with microfabricated, graphene-based VRRAM is investigated for circuit and algorithmic levels. By exploiting a sub-nm thin 2D material, the VRRAM array demonstrates an improved read/write margins and read inaccuracy level for the weighted-sum procedure. Moreover, energy consumption is significantly reduced in array programming operations. Finally, an XNOR logic-inspired architecture designed to integrate 1-bit ternary precision synaptic weights into graphene-based VRRAM is introduced. Simulations on VRRAM with metal and graphene word-planes demonstrate 83.5 and 94.1% recognition accuracy, respectively, denoting the importance of material innovation in neuromorphic computing.

Stimuli‐Responsive Memristive Materials for Artificial Synapses and Neuromorphic Computing

2021 ◽  
pp. 2006469
Author(s):  
Hongyu Bian ◽  
Yi Yiing Goh ◽  
Yuxia Liu ◽  
Haifeng Ling ◽  
Linghai Xie ◽  
...  
Keyword(s):  
Stimuli Responsive ◽  
Neuromorphic Computing
Sign in / Sign up

Export Citation Format

Share Document