A Novel Defect Inspection System Using Convolutional Neural Network for MEMS Pressure Sensors

Abstract

Defect inspection using imaging-processing techniques, which detects and classifies manufacturing defects, plays a significant role in the quality control of microelectromechanical systems (MEMS) sensors in the semiconductor industry. However, high-precision classification and location are still challenging because the defect images that can be obtained are small and the scale of the different defects on the picture of the defect is different. Therefore, a simple, flexible, and efficient convolutional neural network (CNN) called accurate-detection CNN (ADCNN) to inspect MEMS pressure-sensor-chip packaging is proposed in this paper. The ADCNN is based on the faster region-based CNN, which improved the performance of the network by adding random-data augmentation and defect classifiers. Specifically, the ADCNN achieved a mean average precision of 92.39% and the defect classifier achieved a mean accuracy of 97.2%.

Deep-Learning-Guided Evaluation Method for the High-Volume Preparation of Flexible Sensors Based on Inkjet Printing

“…Neural architecture search (NAS) is an emerging technology based on intelligent algorithms to build different types of structures. , Yang et al developed a surface defect classification scheme based on NAS technology, called NAS-SDC, with simpler architecture and higher classification accuracy. The method used only 0.35 M network parameters, and the average test time per sample is about 61 ms, thus achieving a balance between performance and speed.…”