The successful development of Actinic Pattern Mask Inspection (APMI) has enabled the high-volume manufacturing of advanced chips, such as N5 and N3, due to the production of defect-free masks by tsmc's mask shop. This accomplishment can be attributed to the utilization of an innovative Extreme Ultraviolet (EUV) inspector and Graphics Processing Unit (GPU)-based defect detection with Artificial Intelligence (AI) assistance. The application of EUV inspector unleashed pellicle inspection to prolong mask operation periods in wafer foundries. Besides, the improving in the manufacturing efficiency via automation also boost the productivity in the mask shop. According to our previous report in BACUS 2023, the improvement by performing various approaches in the novel Laser-Produced Plasma (LPP) system enabled tsmc to capture EUV image with high stability. The continual improving in the system in later keep reducing the vibration of the crucible and hence improve the tin stability. Furthermore, tsmc developed a GPU-based inspection system, which allowed for flexible algorithm development compared to FPGA. The ML-based rendering model aligned features with tool images and reduced image residue. Therefore, the final inspected image could be possessed with high SNR in advanced node and aggressive OPC compared to DUV inspector. Additionally, the final inspection results will be processed via a Deep Learning (DL) based model, reducing false positives, and implementing auto-defect classification. By combining these contributions, the actinic tools were able to streamline the manufacturing flow and fulfill the requirements for massive production significantly.
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Chang et al. (2024) studied this question.
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