Deep Learning for Advanced Image-Based Semiconductor Metrology

As EUV based lithography gets adopted to keep scaling semiconductor devices in our chips, new metrology and inspection challenges arise. We need to measure these small dimensions fast but without losing accuracy and repeatability. Metrology and inspection are at the heart of process control. Without adequate metrology and inspection capability yields suffer. Conventional process and metrology tools, used in the industry, while generating a lot of data, do not always use them in feed-forward and feed-back cycles. However, there is a continuous need for better insight into process control by using this massive data. The sources of these data may range from tool process logs to lab metrology to computational to FAB metrology inspection. Manual supervision, analysis and finding any relevant inter and /or intra-correlation between these monstrous data sources is nearly impossible and therefore requires better data analysis methods and advanced machine learning techniques. The goal of this project is to use data from the manufacturing tools and use them for building models for better process control and correlate with the electrical performance of devices. The PhD candidate will learn conventional process flow and will be responsible to work collaboratively toward developing and applying “machine learning' based optimization algorithms with a goal to tackle the aforementioned challenges in terms of 1) Reducing computational cost, 2) reduce tool cycle time, 3) predictive process control approach in enabling advanced node semiconductor manufacturing, and 4) improving metrology data. Machine learning applicability includes: 1. Brainstorm “Technical diligence” of the project: to meet desired performance and engineering timeline. 2. Tool data analysis: Collect data, analyse data, and suggest hypothesis with expertise feedback loop. 3. Image processing applicability: Collect Image data (SEM/TEM/EDR/..), suggest ML based hypothesis to extract improved SEM based measurements. 4. Machine learning modelling – build from scratch or improving an existing algorithm for a given application. 5. Collaboration on patent/publications and presentations at international conferences/journals. 6. Supervision of master theses related to the subject of this PhD.