Mask, process, and lithography-tool parameters are the most important aspects for achieving high lithography performance. Various methods have been proposed to optimize these parameters in recent years. Optical proximity correction is the main technology to optimize the mask pattern to enhance the resolution.1 Source mask optimization (SMO) improves the lithography performance by co-optimizing the illumination source and mask.2 The optimized mask structure and source shape via SMO suffers from extreme complexity, especially for the pixelated mask and source,3–5 which leads to difficulty in manufacturing. Furthermore, the parameters related to the process, such as the film stacks, postexposure bake (PEB), and photoresist development, have a strong impact on the process window (PW).6,7 However, most published optimization technologies were implemented under fixed process conditions.3,8,9 Moreover, lithography-tool parameters such as the numerical aperture (NA) and source parameter also determine the PW.1 Actually, the parameters related to the mask, process, and lithography tool simultaneously impact the lithography performance.10,11 The lithography effects caused by multiple parameter errors could compensate for each other,12 indicating that the co-optimization of multiple parameters could improve the PW. However, no effective methods have been published for co-optimizing the mask, process, and lithography-tool parameters.