Thin-film Nitrate Sensor Performance Prediction Based on Preprocessed Sensor Images

Abstract

Automating the assessment of sensor quality in the production of thin-film nitrate sensors can yield significant advantages. Currently, the inspection process is extremely time and labor intensive, requiring technicians to manually examine sensors from each batch to determine their performance. Not only is manually examining sensors costly, it also takes days to conclude the results. It is possible to utilize image based learning approach to entirely automate the quality assessment process by accurately predicting the performance of every sensor; this allows for instant performance analysis and rapid changes to the fabrication parameters.The fabrication parameters will directly control the thickness of the ion-selective membrane (ISM) of the nitrate sensor. The thickness of the ISM directly affects the texture on the sensor’s surface. Because of the reliable correlation between sensor performance and sensor surface texture, it allows us to use learning methods to predict sensor performance through images instead of direct measurements.We propose a method to predict sensor quality using noncontact sensor images through a series of image processing techniques followed by machine and deep learning.