LithoPW: Leveraging Visual Memory Encoding and Defect-Aware Optimization for Precise Determination of the Lithography Process Windows

Lithography stands as a critical step in the manufacturing of integrated circuits, where the precise control of focus and exposure dose parameters is vital for optimal results. The conventional methodologies for defining lithography process windows often face difficulties with managing measurement errors, detecting printed defects, and exploiting visual features from Scanning Electron Microscope (SEM) images. This paper proposes LithoPW, a novel framework that utilizes visual features of SEM images for the determination of process windows. This approach is comprised of a denoising module, a Transformer-based visual memory encoder, and a defect-aware process window optimization module. The denoising module incorporates a Transformer architecture to mitigate the impact of noise, thereby enhancing the efficiency of downstream tasks in leveraging information embedded within SEM images. The transformer-based visual memory encoder discerns each SEM image as a Query, maintaining neighbouring SEM images in memory as Key and Value elements, thereby facilitating precise lithography quality classification associated with the query image. The defect-aware process window optimization module heightens the reliability of the results by adjusting the process window according to the defects identified within the SEM images. Experimental results confirm the efficacy of our framework, highlighting its promising application in lithography production for accurate process window determination.