Ultra-high-efficient Writing in Voltage-Control Spintronics Memory(VoCSM); the Most Promising Embedded Memory for Deep Learning
- Resource Type
- Conference
- Authors
- Ohsawa, Y.; Yoda, H.; Shimomura, N.; Shirotori, S.; Fujita, S.; Koi, K.; Altansargai, B.; Oikawa, S.; Shimizu, M.; Kato, Y.; Inokuchi, T.; Sugiyama, H.; Ishikawa, M.; Ajay, T.; Ikegami, K.; Takaya, S.; Kurobe, A.
- Source
- 2018 IEEE 2nd Electron Devices Technology and Manufacturing Conference (EDTM) Electron Devices Technology and Manufacturing Conference (EDTM), 2018 IEEE 2nd. :214-216 Mar, 2018
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Power, Energy and Industry Applications
Signal Processing and Analysis
Integrated circuits
Electrodes
Writing
Spintronics
Switches
Conferences
Electron devices
- Language
Our new proposal of voltage-control spintronics memory (VoCSM) in which spin-orbit torque (SOT) in conjunction with the voltage-control-magnetic-anisotropy (VCMA) effect works as the writing principle showed small switching current of 37\ μ\ A} for about 350 K_{B}T switching energy. This indicates VoCSM's writing efficiency is so high that VoCSM would be applicable for deep learning (DL) memories requiring ultra-low energy consumption.