TY - GEN
T1 - Gate-Tunable Neuromorphic Devices Enabled by Two-Dimensional Materials
AU - Hersam, Mark C.
N1 - Funding Information:
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
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - (neuromorphic computing
KW - 2D materials
KW - continuous learning)
KW - Gaussian transistors
KW - memtransistors
KW - van der Waals heterojunctions
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U2 - 10.1109/EDTM53872.2022.9798164
DO - 10.1109/EDTM53872.2022.9798164
M3 - Conference contribution
AN - SCOPUS:85133963276
T3 - 6th IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2022
SP - 154
EP - 156
BT - 6th IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2022
Y2 - 6 March 2022 through 9 March 2022
ER -