Reducing the process variation is a significant concern for resistive random access memory(RRAM).Due to its ultrahigh integration density,RRAM arrays are prone to lithographic variation during the lithography process,...Reducing the process variation is a significant concern for resistive random access memory(RRAM).Due to its ultrahigh integration density,RRAM arrays are prone to lithographic variation during the lithography process,introducing electrical variation among different RRAM devices.In this work,an optical physical verification methodology for the RRAM array is developed,and the effects of different layout parameters on important electrical characteristics are systematically investigated.The results indicate that the RRAM devices can be categorized into three clusters according to their locations and lithography environments.The read resistance is more sensitive to the locations in the array(~30%)than SET/RESET voltage(<10%).The increase in the RRAM device length and the application of the optical proximity correction technique can help to reduce the variation to less than 10%,whereas it reduces RRAM read resistance by 4×,resulting in a higher power and area consumption.As such,we provide design guidelines to minimize the electrical variation of RRAM arrays due to the lithography process.展开更多
To improve the identification for visual defect of TFF-LCD, a new machine vision system is proposed, which is superior to human eye inspection. The system respectively employs a CCD camera to capture the image of TFT-...To improve the identification for visual defect of TFF-LCD, a new machine vision system is proposed, which is superior to human eye inspection. The system respectively employs a CCD camera to capture the image of TFT-LCD panel and an image processing system to identify potential visual defects. Image pre-processing, such as average filtering and geometric correction, was performed on the captured image, and then a candidate area of defect was segmented from the background. Feature information extracted from the area of interest entered a fuzzy rule-based classifier that simulated the defect inspection of TFT-LCD undertaken by experienced technicians. Experiment results show that the machine vision system can obtain fast, objective and accurate inspection compared with subjective and inaccurate human eye inspection.展开更多
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supported in part by the Open Fund of State Key Laboratory of Integrated Chips and Systems,Fudan Universityin part by the National Science Foundation of China under Grant No.62304133 and No.62350610271.
文摘Reducing the process variation is a significant concern for resistive random access memory(RRAM).Due to its ultrahigh integration density,RRAM arrays are prone to lithographic variation during the lithography process,introducing electrical variation among different RRAM devices.In this work,an optical physical verification methodology for the RRAM array is developed,and the effects of different layout parameters on important electrical characteristics are systematically investigated.The results indicate that the RRAM devices can be categorized into three clusters according to their locations and lithography environments.The read resistance is more sensitive to the locations in the array(~30%)than SET/RESET voltage(<10%).The increase in the RRAM device length and the application of the optical proximity correction technique can help to reduce the variation to less than 10%,whereas it reduces RRAM read resistance by 4×,resulting in a higher power and area consumption.As such,we provide design guidelines to minimize the electrical variation of RRAM arrays due to the lithography process.
文摘To improve the identification for visual defect of TFF-LCD, a new machine vision system is proposed, which is superior to human eye inspection. The system respectively employs a CCD camera to capture the image of TFT-LCD panel and an image processing system to identify potential visual defects. Image pre-processing, such as average filtering and geometric correction, was performed on the captured image, and then a candidate area of defect was segmented from the background. Feature information extracted from the area of interest entered a fuzzy rule-based classifier that simulated the defect inspection of TFT-LCD undertaken by experienced technicians. Experiment results show that the machine vision system can obtain fast, objective and accurate inspection compared with subjective and inaccurate human eye inspection.
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.
