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Machine Learning-Assisted E-jet Printing of Organic Flexible Biosensors

2021-11-07 · Mehran Abbasi Shirsavar, Mehrnoosh Taghavimehr, Lionel J. Ouedraogo, Mojan Javaheripi, Nicole N. Hashemi, Farinaz Koushanfar, Reza Montazami

Electrohydrodynamic-jet (e-jet) printing technique enables the high-resolution printing of complex soft electronic devices. As such, it has an unmatched potential for becoming the conventional technique for printing soft electronic devices. In this study, the electrical conductivity of the e-jet printed circuits was studied as a function of key printing parameters (nozzle speed, ink flow rate, and voltage). The collected experimental dataset was then used to train a machine learning algorithm to establish models capable of predicting the characteristics of the printed circuits in real-time. Precision parameters were compared to evaluate the supervised classification models. Since decision tree methods could not increase the accuracy higher than 71%, more advanced algorithms are performed on our dataset to improve the precision of model. According to F-measure values, the K-NN model (k=10) and random forest are the best methods to classify the conductivity of electrodes. The highest accuracy of AdaBoost ensemble learning has resulted in the range of 10-15 trees (87%).

📄 PDF Abstract BibTeX arXiv:2111.03985

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BIG-bench Machine LearningEnsemble Learning

Methods 이 논문이 사용한 방법론

k-NN $k$-Nearest Neighbors is a clustering-based algorithm for classification and regression. It is a a type of instance-based learning as it does not attempt to construct a…

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