Fig. 1: Workflow comparison between speech and device characteristic classification. | npj 2D Materials and Applications

Fig. 1: Workflow comparison between speech and device characteristic classification.

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

Fig. 1

The initial steps of the data representation for training: a the audio signal and b the current signal (i) in the time domain and (ii) in the frequency domain (the darker color, the smaller value). c The inference steps after ML for (i) speech recognition and (ii) materials/characteristics classification. d Side views of the device structures, which are fabricated using various materials after being subjected to external factors such as e-beam irradiation, triethanolamine (TEOA) chemical doping, and temperature variations; Au, Ti, Pt, and Cr are used as the source/drain contact metals; silicon dioxide (SiO2) and hexagonal boron nitride (h-BN) are used as the gate dielectrics; the channels are composed of a combination of various atoms such as Mo, W, S, Se, Te, C, and black phosphorus (BP); MoS2, MoTe2, WSe2, ReS2, graphene, and BP have thicknesses varying from monolayer through to 40 layers.

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