Smarter Etch Defect Monitoring by Risk-Based Sampling

Abstract

The semiconductor industry is constantly evolving, and as the demand for more advanced devices grows, so does the need for enhanced quality and reliability solutions. One of the primary challenges in semiconductor fabrication is to minimize the exposure of material to defects. This paper evaluates a solution which utilizes fault detection and classification (FDC) for defect monitoring in dry plasma etch processes, specifically during certain known periods of increased failure rate of a components lifetime. Importantly, the end proposed solution is designed to achieve an optimized volume of sampling by leveraging a risk profile as defined in FDC to ensure defect inspection coverage which matches the risk profile of the component. This paper provides a detailed description of the proposed solution, including evaluation, benefits, and limitations.