Fig. 2: Flowchart for learning and classifying characteristics of 2D transistors. | npj 2D Materials and Applications

Fig. 2: Flowchart for learning and classifying characteristics of 2D transistors.

From: Multiple machine learning approach to characterize two-dimensional nanoelectronic devices via featurization of charge fluctuation

Fig. 2

a Schematic of a 2D layered FET, which was measured at a given VD and VG under various conditions in the shielded state; b amplification of the measured current signal using a low-noise current amplifier; c process of feature engineering the input data into a suitable representation (current MFCCs) through MFCC (the darker color, the smaller value); d ML with HMM algorithms using the current MFCCs, which comprises the 2D array; e deep learning process to re-learn into neural network (NN) through the score vector (Y) extracted via ML with HMM the algorithm using current MFCCs; and f inference steps of device conditions (channel material, gate material, chemical doping, and e-beam irradiation) through ML with the HMM algorithm and deep learning with the NN algorithm.

Back to article page