@article{db4f4802d6d94c268da12ea9d1086212,
title = "Reconfigurable MoS2Memtransistors for Continuous Learning in Spiking Neural Networks",
abstract = "Artificial intelligence and machine learning are growing computing paradigms, but current algorithms incur undesirable energy costs on conventional hardware platforms, thus motivating the exploration of more efficient neuromorphic architectures. Toward this end, we introduce here a memtransistor with gate-tunable dynamic learning behavior. By fabricating memtransistors from monolayer MoS2 grown on sapphire, the relative importance of the vertical field effect from the gate is enhanced, thereby heightening reconfigurability of the device response. Inspired by biological systems, gate pulses are used to modulate potentiation and depression, resulting in diverse learning curves and simplified spike-timing-dependent plasticity that facilitate unsupervised learning in simulated spiking neural networks. This capability also enables continuous learning, which is a previously underexplored cognitive concept in neuromorphic computing. Overall, this work demonstrates that the reconfigurability of memtransistors provides unique hardware accelerator opportunities for energy efficient artificial intelligence and machine learning. ",
keywords = "2D materials, artificial intelligence, hardware accelerator, machine learning, neuromorphic computing",
author = "Jiangtan Yuan and Liu, {Stephanie E.} and Ahish Shylendra and {Gaviria Rojas}, {William A.} and Silu Guo and Hadallia Bergeron and Shaowei Li and Lee, {Hong Sub} and Shamma Nasrin and Sangwan, {Vinod K.} and Trivedi, {Amit Ranjan} and Hersam, {Mark C.}",
note = "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 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 of the Provost, Office for Research, and Northwestern University Information Technology. S.E.L. acknowledges support from a National Science Foundation Graduate Research Fellowship (Grant DGE-1842165). The authors thank Xiaodong Yan for assistance with device characterization. Publisher Copyright: {\textcopyright} ",
year = "2021",
month = aug,
day = "11",
doi = "10.1021/acs.nanolett.1c00982",
language = "English (US)",
volume = "21",
pages = "6432--6440",
journal = "Nano Letters",
issn = "1530-6984",
publisher = "American Chemical Society",
number = "15",
}