Compact Model Build Upon Dynamic Weighting Artificial Neural Network Approach for Complementary Field Effect Transistors-Reference-Cited by-同舟云学术

Compact Model Build Upon Dynamic Weighting Artificial Neural Network Approach for Complementary Field Effect Transistors

Published:2024-01 Issue:1 Volume:71 Page:246-253
ISSN:0018-9383
Container-title:IEEE Transactions on Electron Devices
language:
Short-container-title:IEEE Trans. Electron Devices

Author:

Butola Rajat¹^ORCID,Li Yiming²^ORCID,Kola Sekhar Reddy¹^ORCID

Affiliation:

1. Parallel and Scientific Computing Laboratory, Electrical Engineering and Computer Science International Graduate Program, National Yang Ming Chiao Tung University, Hsinchu, Taiwan

2. Department of Electronics and Electrical Engineering, Parallel and Scientific Computing Laboratory, Electrical Engineering and Computer Science International Graduate Program, the Institute of Communications Engineering, the Institute of Biomedical Engineering, Institute of Pioneer Semiconductor Innovation, and the Institute of Artificial Intelligence Innovation, National Yang Ming Chiao Tung University, Hsinchu, Taiwan

Funder

National Science and Technology Council (NSTC), Taiwan

“2022 Qualcomm Taiwan Research Program, National Yang Ming Chiao Tung University (NYCU),”

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Electronic, Optical and Magnetic Materials

Link

http://xplorestaging.ieee.org/ielx7/16/10382412/10260244.pdf?arnumber=10260244

Reference26 articles.

1. 3-D Self-aligned Stacked NMOS-on-PMOS Nanoribbon Transistors for Continued Moore’s Law Scaling

2. Extension and source/drain design for high-performance finFET devices

3. Impact of Corner Rounding on Quantum Confinement in GAA Nanosheet FETs for Advanced Technology Nodes

4. Device Exploration of NanoSheet Transistors for Sub-7-nm Technology Node

5. Performance Analysis on Complementary FET (CFET) Relative to Standard CMOS with Nanosheet FET