SHF: Small: Data Learning Framework for Diagnosis Based Yield Optimization

University Of California-Santa Barbara, Santa Barbara CA

Investigators

Abstract

ABSTRACT In the semiconductor industry, manufacturing yield, measured as the percentage of salable products produced, is a key metric that determines the financial success of a product line. Low yield translates into increased design cost, delayed time-to-market, and reduced productivity. When low yield occurs, tremendous engineering resources are spent to diagnose and resolve the problems. This project proposes to develop a novel data learning framework that greatly improves the efficiency and effectiveness of the diagnosis and resolution process. The framework consists of a newly developed software infrastructure that interfaces with the existing Electronic Design Automation (EDA) and silicon test software infrastructures, through the design and silicon test data they produce. A collection of data learning software tools and methodologies that analyze said data are utilized to automatically extract knowledge for yield improvement. The research is integrated with educational activities to develop course and tutorial materials released to the industry for broad impact, a state-of-the-art laboratory for education, and a research program to attract undergraduate and underrepresented students. The research strives to achieve a comprehensive understanding of state-of-the-art design and manufacturing practices including anticipated issues in the future, and to accomplish multidisciplinary studies merging knowledge from EDA, silicon test, data mining, and machine learning. Knowledge discovered through this research will provide the industry with a clear direction on where to invest resources to better cope with yield related issues in future ultra nanometer manufacturing technologies. The framework is designed to efficiently improve yield, which helps improve productivity in the semiconductor design industry.