Ferroelectric FET analog synapse for acceleration of deep neural network training
By M. Jerry, Pai-Yu Chen, Jianchi Zhang, Pankaj Sharma, K. Ni, Shimeng Yu, Supriyo Datta. International Electron Devices Meeting, 2017
446 citations recorded by the source corpus.
Subfields: Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/4ab663265a14e6db6b92813686b5ddb9cb8f88ce
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Electrical and Electronic Engineering
- Shimeng Yu
- Huaqiang Wu
- Bin Gao
- He Qian
- Jianshi Tang
- Wenqiang Zhang
- Gert Cauwenberghs
- Daniele Ielmini
Citations in the corpus
- Neuro-inspired computing chips (2020, background): “…can be categorized into two-terminal devices, such as RRAM 12 , phase change memory (PCM) 23 , magnetic random-access memory (MRAM) 48 , and three-terminal devices, such as flash memory 28 , ferroelectric field-effect transistor (FeFET) 49 and electrochemical random-access memory (ECRAM) 50 .”
- Neuro-Inspired Computing With Emerging Nonvolatile Memorys (2018, methodology): “from [88].”
- A compute-in-memory chip based on resistive random-access memory (2022, methodology): “These techniques can be more generally applied to other non-volatile resistive memory technologies such as phase-change memory 8,17,21,23,24 , magnetoresistive RAM 48 and ferroelectric field-effect transistors 49 .”