OVERVIEW

This project focuses on leveraging machine learning techniques to enhance the identification of defects in silicon-based devices during the manufacturing process. By utilizing vector machine algorithm (SVM), a supervised learning model, the project aims to classify and detect various types of silicon defects, such as those affecting performance, yield, or reliability, from parametric test data. The SVM algorithm is trained on labeled datasets of known defect signatures and their corresponding electrical measurements, enabling accurate predictions of potential defects in new silicon samples.
This approach can improve defect localization and decision-making processes, ultimately leading to highermanufacturing efficiency and reduced production costs.

Key features

1.   Machine Learning Integration: Utilizes SVM, a robust supervised learning algorithm, to classify and predict silicon  defects based on test data.
2.   Defect Signature Recognition: Identifies and classifies various silicon defects, such as electrical, structural, or parametric anomalies, by learning from labeled datasets of known defect signatures.
3.   Parametric Data Analysis: Analyzes parametric test data, such as voltage, current, and resistance, to detect subtle anomalies that may indicate defects in silicon wafers or chips.
4.   Improved Defect Localization: Enhances the ability to locate defects with greater accuracy, helping in the early detection and correction of manufacturing issues.
5.   Data-Driven Insights: Provides insights into the nature and causes of defects, enabling data-driven optimization of fabrication processes.
6.   Scalability: The model can be scaled and adapted to detect defects across different semiconductor technologies and fabrication processes. 

Applications

• Semiconductor Manufacturing and Wafer Inspection: Enhances the quality control process by detecting silicon defects during chip fabrication, improving yield, and reducing the cost of rework or scrap.
• Device Reliability Testing: Assists in detecting defects that could affect the long-term reliability of devices, crucial for high-reliability applications like aerospace, automotive, and medical electronics.

Project requirements

•    Python: Familiarity with Python programming language and machine learning libraries like PyTorch.
•    Machine Learning: Familiarity with training, tuning, and evaluating machine learning models.