Fig. 4: Engineered current MFCC study and classification accuracy. | npj 2D Materials and Applications

Fig. 4: Engineered current MFCC study and classification accuracy.

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

Fig. 4

a LF part of current MFCC (the darker color, the smaller value) converted at the same \(\overline {I_{\mathrm{D}}}\) ≈ 1 μA in each class ((i) graphene on trench structure, (ii) MoS2 on SiO2, (iii) MoS2 on SiO2 after e-beam irradiation, and MoS2 on h-BN at (iv) T = 25 K, (v) T = 100 K, and (vi) T = 200 K, respectively); b frequency distribution for a specific interval by normalizing the LF part of current MFCC; c classification accuracies and processing time obtained using the three learning and classification methods based on the number of data; d classification accuracies according to each class (the inset shows a confusion matrix and the darker color, the smaller value); e variations in the measured normalized drain current spectral densities for ReS2 and MoS2 on h-BN at f = 10 Hz and data fitted using the CNF and CNF-CMF models.

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