Finding defects now requires multiple data types, and a much more thorough search.
An explosion in design complexity, fueled by increased transistor density and fundamental shifts in chip architectures, are beginning to overwhelm traditional approaches to test. Defects can show up in the clock trees that drive scan chains, and even inside blocks of scan cells, which may number in the millions. Jayant D’Souza, technical product director for yield learning products in Siemens EDA’s Tessent group, talks about how the shift to backside power delivery and continued device scaling are driving changes in the testing process, and how machine learning can be leveraged to build paretos for different types of defects.
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
Leave a Reply