Mean time between failure of semiconductor- fabrication equipment using data analytics with natural-language processing

A method comprising:

• providing a virtual assistant, a natural language processing (NLP) engine, and a data-analytics engine in communication with a semiconductor-manufacturing system, the semiconductor-manufacturing system comprising a wafer handling system configured to hold one or more wafers for processing, wafer processing components configured to physically treat the one or more wafers, and a controller configured to operate the processing components;
• receiving, by the virtual assistant from a user, a user query related to at least one wafer processing component of the wafer processing components;
• processing, by the NLP engine, the user query to understand an intent or context relating to the user query by extracting entities corresponding to the at least one wafer processing component and identifying a relationship between the extracted entities;
• obtaining, by the data-analytics engine, data relating to the intent or context of the user query by predicting relevant data from data collected from the wafer processing components via one or more communication protocols based on the extracted entities and the identified relationship;
• generating, by the data-analytics engine, analytical data by analyzing the obtained data;
• and
• providing, by the virtual assistant, the analytical data to the user in response to the user query,
• wherein the virtual assistant is further configured to:
• identify a cause of yield loss in the wafer processing components based on the analytic data and provide a context-specific response to the user query that comprises information for operating one or more of the wafer processing components to increase yield.