Semiconductor manufacturing is a complex procedure with challenging variations in machines and processes. For example, due to high-mix semiconductor manufacturing, in which hundreds of types of products are manufactured on one production line, the control-process-quality relationships are not fully understood based on quantitative data analysis. Understanding the control-process-quality relationships will enable consistent production by the prevention of faults based on measured process variations. Also, as feature sizes decrease, semiconductor yields decrease and production costs increase. Nondestructive and high-yield critical dimension (CD) metrology is needed for 3D nanostructures and features, such as gate-all-around (GAA) field-effect transistors (FETs) and hybrid Cu-Cu bonds. Accordingly, semiconductor manufacturing systems are needed that rely on augmented intelligence, the augmenting of physics-based intelligence with artificial intelligence (AI). Augmented intelligence is envisioned as a disruptive catalyst for process optimization, component verification, and process control. Specifically, semiconductor manufacturers desire smart production machines that assess and predict their own health and the performance of their processes in real time to optimize production quality and yield. Augmented intelligent solutions use on-machine sensors for measurements before and during manufacturing processes, analyze the data with a fusion of metrological approaches and machine learning, and monitor and predict the performance of machines and their processes.

Proposals are welcome to develop augmented intelligent solutions for semiconductor manufacturing systems. For example, new methods may be developed for wafer inspection, control-process-quality relationships, advanced packaging, and non-planar nanoscale transistor and hybrid bond metrology, among many other possible topics. Our facilities support experimental work via laboratory testbeds, numerous machining centers, and a nanoscale science center. Furthermore, NIST provides computational resources and has an interest group for AI that regularly meets, giving the successful applicant an opportunity to interact with a variety of NIST engineers and scientists.

Keywords:

Semiconductor manufacturing; Advanced packaging; Smart manufacturing; Intelligent manufacturing; Advanced manufacturing; Subtractive manufacturing; Data-driven manufacturing; Manufacturing; Production; Industry 4.0; Advanced intelligence; Artificial intelligence; Data-driven science; Machine learning; Augmented intelligence; Machines; Machine tools; Production machines; Semiconductors; Measurement science; Metrology; Sensors; Technology transfer

Citizenship:  Open to U.S. citizens

Level:  Open to Postdoctoral applicants