Bubble detection in photoresist with small samples based on GAN augmentations and modified YOLO

Photoresist coating is a key procedure in semiconductor wafer surface processing, and bubbles in a photoresist dropper usually affect the uniformity of photoresist film, which seriously reduces the etching quality of semiconductor wafer surface. However, the problems of blurred edges of photoresist bubbles and complex corrugations in photoresist itself make the automatic detection of photoresist bubbles very challenging. To overcome this problem, a high-precision photoresist bubble detection method based on deep learning is proposed in this paper. First, aiming at the problem that the photoresist bubble samples are extremely lacking and cannot meet the requirement of deep learning model training, this paper proposes a sample automatic generator bubbleGAN based on the adversarial generative network, so as to effectively increase the number of photoresist bubble samples with better diversity of the target to be detected, thus improving the performance of the detection model; Second, aiming at the challenges of target detection caused by the blurred edges of photoresist bubbles and the complex ripples of photoresist itself, this paper proposes a novel bubble detection method mYOLO. Based on the recently developed high-performance target detection method YOLOX, we optimize its structure and activation function to reduce the difficulty of model training, solve the dead zone problem of activation function, and improve the accuracy of model detection. The experimental results on the dataset collected by the actual rotary coating machine prove the effectiveness of the proposed algorithm. In addition, we further compare the impact of different manual data augmentation methods on the detection model.