Please use this identifier to cite or link to this item: https://doi.org/10.1088/1361-6528/abd655
Title: Deep learning-enabled prediction of 2D material breakdown
Authors: Huan, Yan Qi
Liu, Yincheng
Goh, Kuan Eng Johnson 
Wong, Swee Liang 
Lau, Chit Siong
Keywords: Science & Technology
Technology
Physical Sciences
Nanoscience & Nanotechnology
Materials Science, Multidisciplinary
Physics, Applied
Science & Technology - Other Topics
Materials Science
Physics
machine learning
convolutional neural network
long short-term memory
electric breakdown
transition metal dichalcogenides
molybdenum disulfide
field-effect transistor
FIELD-EFFECT TRANSISTORS
MONOLAYER MOS2
ELECTRICAL BREAKDOWN
INTEGRATED-CIRCUITS
GRAPHENE
Issue Date: 25-Jun-2021
Publisher: IOP PUBLISHING LTD
Citation: Huan, Yan Qi, Liu, Yincheng, Goh, Kuan Eng Johnson, Wong, Swee Liang, Lau, Chit Siong (2021-06-25). Deep learning-enabled prediction of 2D material breakdown. NANOTECHNOLOGY 32 (26). ScholarBank@NUS Repository. https://doi.org/10.1088/1361-6528/abd655
Abstract: Characterizing electrical breakdown limits of materials is a crucial step in device development. However, methods for repeatable measurements are scarce in two-dimensional materials, where breakdown studies have been limited to destructive methods. This restricts our ability to fully account for variability in local electronic properties induced by surface contaminants and the fabrication process. To tackle this, we implement a two-step deep-learning model to predict the breakdown mechanism and breakdown voltage of monolayer MoS2 devices with varying channel lengths and resistances using current measured in the low-voltage regime as inputs. A deep neural network (DNN) first classifies between Joule and avalanche breakdown mechanisms using partial current traces from 0 to 20 V. Following this, a convolutional long short-term memory network (CLSTM) predicts breakdown voltages of these classified devices based on partial current traces. We test our model with electrical measurements collected using feedback-control of the applied voltage to prevent device destruction, and show that the DNN classifier achieves an accuracy of 79% while the CLSTM model has a 12% error when requiring only 80% of the current trace as inputs. Our results indicate that information encoded in the current behavior far from the breakdown point can be used for breakdown predictions, which will enable non-destructive and rapid material characterization for 2D material device development.
Source Title: NANOTECHNOLOGY
URI: https://scholarbank.nus.edu.sg/handle/10635/202309
ISSN: 09574484
13616528
DOI: 10.1088/1361-6528/abd655
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