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This paper addresses the quality control of the photolithography process in the semiconductor industry. Overlay errors in the process seriously affect the wafer yield, and cause the wafer to be forced to rework and affect the production efficiency of the equipment. We examine the current state of its process control, develop a novel overlay predict model, and verify the prediction results. This study proposes a Global Attention Generative Adversarial Networks (GAGAN) model to precisely predict the overlay error for the feed-forward data of the front layer, which is used as the important information and process parameters for the advanced process control of the current layer. Experiment results on a semiconductor shop-floor confirms that our proposed method achieves high predictive performance while maintaining extensibility and visual quality. [ABSTRACT FROM AUTHOR]

The targeting of transistor gate length is a primary driver of device performance. The targeting of the physical gate critical dimension (CD) greatly impacts the electrical gate dimension. Traditionally, continual monitoring and manual offsets to compensate for lithographic and etch equipment variability have been used to control gate CDs. This paper discusses how advanced process control techniques were applied to the 0.13-μm polysilicon (poly) patterning process. Both scanner and etch equipment were controlled using a combination of feedforward and feedback loops. As a result, significant engineering labor was saved, and gate CD 3 sigma results improved 12%, correlating to improved device performance and enhanced yield. [ABSTRACT FROM AUTHOR]