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

TL;DR

This paper demonstrates how machine learning models can predict the electrical conductivity of e-jet printed organic biosensors, optimizing printing parameters for better device performance.

Contribution

It introduces a machine learning approach to predict printed circuit conductivity based on printing parameters, enhancing real-time quality control in e-jet printing.

Findings

K-NN and random forest are effective classifiers for conductivity.

AdaBoost achieved up to 87% accuracy with 10-15 trees.

Decision trees alone had limited accuracy (~71%).

Abstract

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%).