Autofocus Measurement for Electronic Components Using Deep Regression

Image-based focus measurement in the manufacturing process is important for tasks such as automated inspection, automated assembly, and autosoldering. To inspect or manipulate an object, the visual system must sense the best image position to ensure that all the details of the product can be viewed. For this reason, an autofocus system is required. Traditional autofocus systems based on depth-from-focus (DFF) or depth-from-defocus (DFD) rely on a series of images by moving the lens with a control motor to find the sharpest location. The processing time defers it for in-line, real-time industrial applications. In this article, we propose a deep neural network regressor for fast and accurate autofocus. The convolutional neural network (CNN) model is proposed and evaluated for the autofocus task on a variety of electronic surfaces, including wafers, liquid crystal display (LCD), and ball grid array (BGA). The proposed deep regression model requires only one single sensed image of the object to estimate the depth. The effect of environmental variations in the illumination is evaluated for the robustness of the proposed method. Experimental results indicate the proposed CNN Regressor can achieve fast and accurate results for the tested objects with repetitive and arbitrary patterns.