Strategic design of asymmetric graphene and ReS2 field-effect transistors using nonlinear optimization and machine learning
Abstract
Emerging two-dimensional (2D) materials like graphene and rhenium disulfide (ReS2) offer unique opportunities for developing disruptive electronic technologies beyond conventional silicon. In particular, heterostructure devices that integrate dissimilar 2D materials can achieve superior performance never seen before in traditional transistors. However, rationally designing and actualizing such devices for transforming real-world applications requires addressing multifaceted compromises across critical metrics like switching speed, power density, ...
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