Scanning probe lithography(SPL)is a promising technology to fabricate high-resolution,customized and costeffective features at the nanoscale.However,the quality of nano-fabrication,particularly the critical dimension,...Scanning probe lithography(SPL)is a promising technology to fabricate high-resolution,customized and costeffective features at the nanoscale.However,the quality of nano-fabrication,particularly the critical dimension,is significantly influenced by various SPL fabrication techniques and their corresponding process parameters.Meanwhile,the identification and measurement of nano-fabrication features are very time-consuming and subjective.To tackle these challenges,we propose a novel framework for process parameter optimization and feature segmentation of SPL via machine learning(ML).Different from traditional SPL techniques that rely on manual labeling-based experimental methods,the proposed framework intelligently extracts reliable and global information for statistical analysis to finetune and optimize process parameters.Based on the proposed framework,we realized the processing of smaller critical dimensions through the optimization of process parameters,and performed direct-write nano-lithography on a large scale.Furthermore,data-driven feature extraction and analysis could potentially provide guidance for other characterization methods and fabrication quality optimization.展开更多
基金the financial support from the National Natural Science Foundation of China under Grant(52275564,51875313).
文摘Scanning probe lithography(SPL)is a promising technology to fabricate high-resolution,customized and costeffective features at the nanoscale.However,the quality of nano-fabrication,particularly the critical dimension,is significantly influenced by various SPL fabrication techniques and their corresponding process parameters.Meanwhile,the identification and measurement of nano-fabrication features are very time-consuming and subjective.To tackle these challenges,we propose a novel framework for process parameter optimization and feature segmentation of SPL via machine learning(ML).Different from traditional SPL techniques that rely on manual labeling-based experimental methods,the proposed framework intelligently extracts reliable and global information for statistical analysis to finetune and optimize process parameters.Based on the proposed framework,we realized the processing of smaller critical dimensions through the optimization of process parameters,and performed direct-write nano-lithography on a large scale.Furthermore,data-driven feature extraction and analysis could potentially provide guidance for other characterization methods and fabrication quality optimization.
