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Lithography processes are important as they impact following process steps such as etching and deposition. Additionally, to keep up with the feature size reduction race, the number of steps and process complexity are increasing drastically. To maintain performances and produce masks and wafers with correct specifications, yield can be tracked and understood during R&D and production steps through key elements followed by metrology and defectivity analysis.
We propose a metrospection holistic AI-driven software platform composed of metrology and defectivity capabilities. It is oriented to the analysis of any kind of object on all types of images for lithography. Advanced AI powered object and edge detection algorithms help process engineers to quickly track their features of interest, while proposing a large variety of measurements. An analysis can be easily customized to fit customer needs of measurements without technical assistance, which has the advantage of protecting their internal knowledge and intellectual property (IP). In addition, users can quantify defectivity of the process by defining defects or by tracking anomalies. Users provide compliant images for unsupervised and labeled images for supervised methods. These features are available at larger image scale and full wafer view to control processes from the nanoscale to the microscale. With ever growing complexity and quantity of available data, metrospection is becoming essential for process control. Although some software allows users to detect and classify defects, measurements of defects are needed as indicators of their severity, which can be performed by our platform with freedom of measurement’s customization.
Finally, as confidentiality and IP protection are major subjects, our platform allows users to capitalize on their internal development, to easily integrate their own solutions to their version of the product. Our software includes a plugin manager to allow easy customization and updating of the graphical interface to match each user's needs and benefit from the latest advances and drastically increases process control accuracy and throughput.
In this paper, we propose a new generation of software platform and development infrastructure which can integrate specific metrology business modules. For example, we will show the integration of a chemistry module dedicated to electronics materials like Direct Self Assembly features. We will show a new generation of image analysis algorithms which are able to manage at the same time defect rates, images classifications, CD and roughness measurements with high throughput performances in order to be compatible with HVM. In a second part, we will assess the reliability, the customization of algorithm and the software platform capabilities to follow new specific semiconductor metrology software requirements: flexibility, robustness, high throughput and scalability. Finally, we will demonstrate how such environment has allowed a drastic reduction of data analysis cycle time.
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