Python Automation and Machine Learning for EM and ICs

The first applications of AI tools to FA report classification include a Support Vector Classifier (SVC) [1] with WORD2VEC [2] embeddings, and clustering models, which used the term frequency-inverse document frequency (TF-IDF) approach and WORD2VEC to embed tokens representing words into a vector space and thus obtained satisfactory results [3], the transformer architecture [4], large LMs [5] and Bidirectional Encoder Representations from Transformer (BERT) [6]. Natural Language Processing (NLP) approaches, including automatic translators, recommender systems, or chatbots, can be applied to address Failure Analysis (FA) search problem:

1. Efficiency of Modern NLP Approaches: Modern NLP techniques have demonstrated their efficiency and effectiveness in various applications. These include automatic translators, which can translate text between different languages, recommender systems that suggest relevant products or content, and chatbots that can interact with users in natural language. These successes showcase the power of NLP in understanding and processing human language.

2. Text Classification as a Promising Solution: Among the various NLP applications, text classification stands out as one of the most promising approaches to address the FA search problem. Text classification involves automatically assigning labels or categories to text documents based on their content. In the context of FA, this can be used to associate labels with reports denoting physical or electrical failures, methods used in the analysis, or tools utilized in the process.

3. Automatic Association of Labels with FA Reports: By applying text classification techniques, FA documents and reports can be automatically labeled or categorized. For example, a document describing a specific type of semiconductor failure can be tagged with relevant labels, making it easier for engineers to identify and retrieve information related to that particular failure type.

4. Organizing and Navigating FA Data: The labeled documents enable engineers to organize and navigate the FA data more efficiently. They can use the assigned labels to filter and search for specific types of failures, methods, or tools, simplifying the process of accessing relevant information.

5. Assisting Engineers with Recommendations: NLP-based text classification methods can also assist engineers by recommending similar jobs or failure analysis tasks. By identifying similarities between different reports or cases, the system can suggest relevant resources or past solutions, potentially saving time and effort for engineers.

6. Providing Statistics based on Faults, Tools, or Methods: Text classification and NLP techniques can help generate statistics based on the labeled data. Engineers can obtain valuable insights into the distribution of faults, commonly used tools, or preferred methods for failure analysis. This statistical information can aid in decision-making and process improvement.