Meta-Learned and TCAD-Assisted Sampling in Semiconductor Laser Annealing.

While applying machine learning (ML) to semiconductor manufacturing is prevalent, an efficient way to sample the search space has not been explored much in key processes such as lithography, annealing, deposition, and etching. The aim is to use the fewest experimental trials to construct an accurate predictive model. Here, we proposed a technology computer added design (TCAD)-assisted meta-learned sampling approach.

Mapping Oxidation and Wafer Cleaning to Device Characteristics Using Physics-Assisted Machine Learning.

It is always highly desired to have a well-defined relationship between the chemistry in semiconductor processing and the device characteristics. With the shrinkage of technology nodes in the semiconductors roadmap, it becomes more complicated to understand the relation between the device electrical characteristics and the process parameters such as oxidation and wafer cleaning procedures. In this work, we use a novel machine learning approach, i.