Deep learning has an increasing impact on our personal and professional lives. Deep learning has the potential to transform mask, semiconductor and electronics manufacturing. This paper reviews key results from the Center for Deep Learning in Electronics Manufacturing’s (CDLe’s) first year of operation. We consider results from adapting five common types of deep learning recipes to solve key challenges in the manufacture of photomasks, printed circuit boards (PCBs), and flat panel displays (FPDs). These deep learning applications include 1) grouping similar items to automatically categorize mask rule errors; 2) using U-Net architecture to construct fast mask designs; 3) using vision-based object classification to find and classify pick-and-place (PnP) errors on PCB assembly lines; 4) using anomaly detection to improve the quality of FPDs; and 5) using digital twins to create SEM images and optimize Inverse Lithography Technology (ILT). While we compare the relative benefits of these techniques, all show the importance of data to improve the success of deep learning networks and of electronics manufacturing. These applications rely on varying neural network architectures such as autoencoders, segmentation networks, deep convolutional networks, anomaly detection, and generative adversarial networks (GANs).
Deep learning (DL) is one of the fastest-growing fields in artificial intelligence (AI). While still in its early stages of adoption, DL has already shown it has the potential to make significant changes to the lithography and photomask industries through the automation or optimization of equipment and processes. The key element required for application of DL techniques to any process is a large volume of data to adequately train the DL neural networks. The accuracy of the classification or prediction of any DL system is dependent on the depth and breadth of the training data to which it is exposed. For semiconductor manufacturing, finding adequate data – especially for corner cases – can be difficult and/or expensive. In this paper, we will present two digital twins that are themselves built from DL as a part of a DL Starter Kit. We will demonstrate the creation of DL-based digital twins for a mask scanning electron microscope (SEM) and for curvilinear inverse lithography technology (ILT).