Abstract
Neuromorphic (i.e., brain-like) computing aims to circumvent the limitations of von Neumann architectures by spatially co-locating processor and memory blocks or even combining logic and data storage functions within the same device. Neuromorphic devices also have the potential to provide efficient architectures for image recognition, machine learning, and artificial intelligence. With this motivation in mind, this paper will explore how the unique materials properties of two-dimensional (2D) materials enable opportunities for novel gate-tunable neuromorphic devices.
Original language | English (US) |
---|---|
Title of host publication | 6th IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2022 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 154-156 |
Number of pages | 3 |
ISBN (Electronic) | 9781665421775 |
DOIs | |
State | Published - 2022 |
Event | 6th IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2022 - Virtual, Online, Japan Duration: Mar 6 2022 → Mar 9 2022 |
Publication series
Name | 6th IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2022 |
---|
Conference
Conference | 6th IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2022 |
---|---|
Country/Territory | Japan |
City | Virtual, Online |
Period | 3/6/22 → 3/9/22 |
Funding
This research was primarily supported by the National Science Foundation Materials Research Science and Engineering Center at Northwestern University (Grant NSF DMR-1720139). Device fabrication was partially funded by the Laboratory Directed Research and Development Program at Sandia National Laboratories (SNL). SNL is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia LLC, a wholly owned subsidiary of Honeywell International Inc. for the U.S. DOE National Nuclear Security Administration under Contract DE-NA0003525. This work describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. DOE or the United States Government. This work also made use of the Northwestern University NUANCE Center and Micro/Nano Fabrication Facility (NUFAB), which has received support from the Soft and Hybrid Nanotechnology Experimental (SHyNE) Resource (Grant NSF ECCS-1542205), the MRSEC program (Grant NSF DMR-1720139) at the Materials Research Center, the International Institute for Nanotechnology (IIN), the Keck Foundation, and the State of Illinois. This research was also supported in part through the computational resources and staff contributions provided by the Quest high performance computing facility at Northwestern University, which is jointly supported by the Office
Keywords
- (neuromorphic computing
- 2D materials
- continuous learning)
- Gaussian transistors
- memtransistors
- van der Waals heterojunctions
ASJC Scopus subject areas
- Process Chemistry and Technology
- Hardware and Architecture
- Electrical and Electronic Engineering
- Industrial and Manufacturing Engineering
- Electronic, Optical and Magnetic Materials