In the dynamic landscape of advanced analytics in semiconductor testing, the choice between traditional statistical data analytics methods and advanced Machine Learning (ML) neural network models is pivotal for ensuring efficient and effective testing processes. This presentation presents a comprehensive framework to aid test engineers in making informed decisions regarding the selection of analytics methodologies.
The framework encompasses various factors including the complexity of the test process, test availability, and the desired outcomes. Traditional data analytics, characterized by its statistical and algorithmic approaches, are well-suited for scenarios where the underlying principles of the test process are well understood, and the data exhibits clear patterns. On the other hand, the latest ML techniques offer unparalleled capabilities in handling complex, high-dimensional data, and are particularly advantageous when dealing with non-linear relationships and uncertainty.
Additionally, Physics-Informed Neural Networks (PINNs) and causal inference (or interpretable AI) are complementary approaches that can significantly enhance decision-making and understanding in semiconductor manufacturing and testing. Combining these methodologies can enhance decision-making in semiconductor manufacturing and testing by providing insights into the underlying physics and causal relationships. Through an analysis of real-world scenarios and case studies, this paper demonstrates the applicability and limitations of both traditional and advanced analytical approaches in the semiconductor test process, offering guidelines for identifying the most appropriate methodology based on the specific requirements and constrains of the testing environment.
A well-known example where neural networks surpass traditional statistical methods, such as Hidden Markov Models (HMMs), is speech recognition. A compelling example in semiconductor testing is fault detection and analysis. Neural networks can analyze vast arrays of sensor data from chip testing procedures to identify subtle patterns indicative of failures, which might elude conventional statistical methods. In addition, a strategy combining PINNs, causal inference, and interpretable AI can be employed to reduce defect occurrences. This involves developing a PINNs model to predict defects based on process parameters, using causal inference to identify causal relationships, and applying interpretable AI techniques to generate human-understandable insights. By providing engineers with tools that predict and explain the impact of process adjustments on defects, informed decision-making is facilitated. By offering insights into this decision-making process, this presentation aims to empower test engineers with knowledge and tools necessary to leverage latest ML techniques effectively, thereby optimizing test processes and enhancing overall semiconductor manufacturing efficiency and reliability.
