AN IMAGE-BASED METHOD TO PREDICT SURFACE ENHANCED RAMAN SPECTROSCOPY SENSOR QUALITY

Image-based quality control is a powerful tool for non-destructive testing of product quality. Machine vision systems (MVS) often implement image-based machine learning algorithms in an attempt to match human level accuracy in detecting product defects for better efficiency and repeatability. Plasmonic sensors, such as those used in Surface Enhanced Raman Spectroscopy (SERS), present a unique challenge for image-based quality control, because in addition to obvious defects such as scratches and missing areas, subtle color changes can also indicate significant changes in sensor performance. As a further challenge, it is not straightforward for even a human expert to distinguish between high- and low-quality sensors based on these subtle color changes on the sensors. In this paper we show that by extracting image features according to the domain knowledge, we can build an image-based method that outperforms human expert prediction. This method enables automated non-destructive SERS sensor quality control and has been implemented successfully on our server.