DEEP LEARNING OF BISMUTH TELLURIDE CRYSTAL SIZE AND GRAIN BOUNDARIES FOR PREDICTIVE PERFORMANCE

1 . A system, comprising:

a database comprising a plurality of training datasets, each training dataset comprising a scanning electron microscope (SEM) image of a semiconductor wafer, an energy-dispersive spectroscopy (EDS/EDAX) image of the wafer, an electron backscatter diffraction (EBSD) image of the wafer, and zT values of semiconductor components of the wafer; and

a processor operable to implement a machine learning module that, when trained with the training datasets, is operable to process an input dataset of another semiconductor wafer through the machine learning module to predict zT values of semiconductor components of the other wafer, the input dataset of the other wafer comprising an SEM image of the other wafer, an EDS/EDAX image of the other wafer, and an EBSD image of the other wafer.

2 . The system of claim 1 , wherein:

the machine learning module is operable to identify grain boundaries of the other wafer.

3 . The system of claim 2 , wherein:

the machine learning module is operable to identify precipitates at the grain boundaries of the other wafer.

4 . The system of claim 1 , wherein:

the machine learning module is operable to identify crystalline properties of the other wafer.

5 . The system of claim 1 , wherein:

the machine learning module comprises at least one of a supervised learning algorithm, a semi-supervised learning algorithm, an unsupervised learning algorithm, a regression analysis algorithm, a reinforcement learning algorithm, a self-learning algorithm, a feature learning algorithm, a sparse dictionary learning algorithm, an anomaly detection algorithm, a generative adversarial network, a convolutional neural network, a transfer learning algorithm, or an association rules algorithm.

6 . The system of claim 1 , wherein:

the semiconductor wafer is configured from bismuth tellurium.

7 . A computer implemented method, comprising:

training a machine learning module with a plurality of training datasets, each training dataset comprising a scanning electron microscope (SEM) image of a semiconductor wafer, an energy-dispersive spectroscopy (EDS/EDAX) image of the wafer, an electron backscatter diffraction (EBSD) image of the wafer, and zT values of semiconductor components of the wafer; and

processing an input dataset of another semiconductor wafer through the machine learning module, the input dataset of the other wafer comprising an SEM image of the other semiconductor wafer, an EDS/EDAX image of the other wafer, and an EBSD image of the other wafer; and

predicting zT values of semiconductor components of the other wafer based on the processing of the input dataset of the other wafer through the machine learning module.

8 . The method of claim 7 , further comprising, via the machine learning module:

identifying grain boundaries of the other wafer.

9 . The method of claim 8 , further comprising, via the machine learning module:

identifying precipitates at the grain boundaries of the other wafer.

10 . The method of claim 7 , further comprising, via the machine learning module:

identifying crystalline properties of the other wafer.

11 . The method of claim 7 , wherein:

the machine learning module comprises at least one of a supervised learning algorithm, a semi-supervised learning algorithm, an unsupervised learning algorithm, a regression analysis algorithm, a reinforcement learning algorithm, a self-learning algorithm, a feature learning algorithm, a sparse dictionary learning algorithm, an anomaly detection algorithm, a generative adversarial network, a convolutional neural network, a transfer learning algorithm, or an association rules algorithm.

12 . The method of claim 7 , wherein:

the semiconductor wafer is configured from bismuth tellurium.

13 . A non-transitory computer readable medium comprising instructions that, when executed by a processor, direct the processor to:

train a machine learning module with a plurality of training datasets, each training dataset comprising a scanning electron microscope (SEM) image of a semiconductor wafer, an energy-dispersive spectroscopy (EDS/EDAX) image of the wafer, an electron backscatter diffraction (EBSD) image of the wafer, and zT values of semiconductor components of the wafer; and

process an input dataset of another semiconductor wafer through the machine learning module, the input dataset of the other wafer comprising an SEM image of the other semiconductor wafer, an EDS/EDAX image of the other wafer, and an EBSD image of the other wafer; and

predict zT values of semiconductor components of the other wafer based on the processing of the input dataset of the other wafer through the machine learning module.

14 . The computer readable medium of claim 13 , further comprising instructions that direct the processor, via the machine learning module, to:

identifying grain boundaries of the other wafer.

15 . The computer readable medium of claim 14 , further comprising instructions that direct the processor, via the machine learning module, to:

identifying precipitates at the grain boundaries of the other wafer.

16 . The computer readable medium of claim 13 , further comprising instructions that direct the processor, via the machine learning module, to:

identifying crystalline properties of the other wafer.

17 . The computer readable medium of claim 13 , wherein:

the machine learning module comprises at least one of a supervised learning algorithm, a semi-supervised learning algorithm, an unsupervised learning algorithm, a regression analysis algorithm, a reinforcement learning algorithm, a self-learning algorithm, a feature learning algorithm, a sparse dictionary learning algorithm, an anomaly detection algorithm, a generative adversarial network, a convolutional neural network, a transfer learning algorithm, or an association rules algorithm.

18 . The computer readable medium of claim 13 , wherein:

the semiconductor wafer is configured from bismuth tellurium.