Abstract
Atomistic quantum transport simulations are used to generate the electronic and transport properties of 10,000 realistic silicene nanoribbons (SiNRs) with edge defects. This ensemble of 20 nm-long and 2.1 nm-wide SiNRs is divided into the training and inference set for the artificial neural network (ANN) employed for the prediction of edge-defect-limited carrier mobility from the known values of bandgap and nanoribbon conductance. We find that an optimized ANN with 3 hidden layers can predict SiNR mobility values and variability histograms with acceptable accuracy, thus providing a useful supplement to atomistic quantum transport simulations that take several hours or days for large device ensemble sizes.
Type
Publication
2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO)
