Dual Entropy-Controlled Convolutional Neural Network for Mini/Micro LED Defect Recognition

Neural network-based computer vision is widely used in industrial image detection due to the outstanding performance of fast and accurate defect recognition, which can be applied to the healthy recognition of mini/micro LED chips. However, limited by optical imaging of industrial cameras and chip physical size, the following challenges exist for mini/micro LED chip defect recognition: 1) the difference between a healthy chip and a defective chip image is small due to the limited size of the chip image, low image resolution, and the few pixels occupied by the defect; 2) standardized mini/micro LED industrial manufacturing produces a limited number of defective products, resulting in an imbalance of positive and negative samples. To overcome these challenges, a dual entropy-controlled convolutional neural network (DENC-CNN) combining feature entropy consistency (FEC) and gradient contribution entropy (GCE) is proposed. FEC is proposed to improve feature enrichment and consistency of information transfer to enhance the learnable of samples, with a few fusion parameters. Global attention multi-scale low-resolution feature fusion (GAMLF) is constructed and combined with FEC to retain the detail of multiscale low-resolution features, enhancing the feature description capability of the model. To deal with the inevitable positive and negative sample imbalance, GCE is designed as part of the gradient weighting to guide the model to pay more attention to hard-to-classify samples, while avoiding over-focusing on hard-to-classify samples and over-fitting. We also construct a mini/micro LED dataset based on self-built image acquisition system to validate the proposed model. Experiments show that the proposed DENC-CNN achieves an accuracy of 99.12%, a G-mean of 97.86% and an F1-score of 97.87% for mini/micro LED chip defect recognition.