The decrease of feature size and the growing complexity of the fabrication process lead to
more failures in manufacturing semiconductor devices. Therefore, identifying the root cause …

Physical failure analysis (PFA) specifies layout designs that affect large-scale integration
(LSI) failure. Because of their capability and cost-effectiveness, convolutional neural …

Root Cause Analysis (RCA) is a critical technology for yield improvement in integrated circuit
manufacture. Traditional RCA prefers unsupervised algorithms such as Expectation …

Software diagnosis is the process of locating and characterizing a defect in a failing chip. It is
the cornerstone of failure analysis that consequently enables yield learning and monitoring …

Device complexity is reaching all-time highs with the adoption of high aspect ratio FinFETs
created using multi-patterning process technologies. Simultaneously, new product segments …

Fault diagnosis plays a major role in IC yield enhancement as it can help identify yield
limiting defects in fabricated devices. The information on such defects is used to guide …

Volume diagnosis has been used effectively to identify systematic defects for yield learning.
Root cause deconvolution (RCD), an unsupervised machine learning technique which uses …

Identifying common root causes of systematic defects in a short time is crucial for yield
improvement. Diagnosis driven yield analysis (DDYA) such as Root cause deconvolution …

Statistical analysis based on layout-aware scan diagnosis has been successfully used for
identifying defect root causes and reducing physical failure analysis (PFA) efforts, especially …

A typical mobile processor die may contain, among other things, a variety of high-
performance as well as low-power processing cores along with 5G modems, Wi-Fi modules …