Abstract: Wafer quality is determined by modeling equipment history as a sequence of events, then evaluating anomalous results for individual events. Identifying an event that generates bad wafers narrows the list of possible root causes.

Abstract: A template for assigning the most probable root causes for wafer defects. The bin map data for a subject wafer can be compared with bin map data for prior wafers to find wafers with similar issues. A probability can be determined as to whether the same root cause should be applied to the subject wafer, and if so, the wafer can be labeled with that root cause accordingly.

Abstract: A sequence of models accumulates r-squared values for an increasing number of variables in order to quantify the importance of each variable to the prediction of a targeted yield or parametric response.

Abstract: A predictive model for equipment fail modes. An anomaly is detected in a collection of trace data, then key features are calculated. A search is conducted for the same or similar anomalies having the same key features in a database of past trace data. If the same anomaly occurred before and is in the database, then the type of anomaly, its root cause, and action steps to correct can be retrieved from the database.

Abstract: Classifying wafers using Collaborative Learning. An initial wafer classification is determined by a rule-based model. A predicted wafer classification is determined by a machine learning model. Multiple users can manually review the classifications to confirm or modify, or to add user classifications. All of the classifications are input to the machine learning model to continuously update its scheme for detection and classification.

Abstract: Detection of data anomalies resulting from maintenance activities on semiconductor processing equipment. Time-series representation of the key indicators for equipment performance is cleaned then segmented according to sharp breaks in the data. The cleaned and segmented data is modeled, for example, by determining a linear fit for each segment. The slope and intercept of each modeled segment linear fit are compared and evaluated to identify anomalies in the data.

Abstract: A semiconductor image classifier. Convolution functions are applied to modify the wafer images in order to extract key information about the image. The modified images are condensed then processed through a series of pairwise classifiers, each classifier configured to determine that the image is more like one of the pair than the other. Probabilities from each classifier are collected to form a prediction for each image.

Abstract: A machine learning model for each die for imputing process control parameters at the die. The model is based on wafer sort parametric measurements at multiple test sites across the entire wafer, as well as yield results for the wafer. This allows for a better analysis of outlier spatial patterns leading to improved yield results.

Abstract: A method of evaluating the microstructure of a surface, such as a coating on a substrate. The surface is illuminated using at least one light source. One or more images of the illuminated surface are captured. The captured images are processed to identify one or more features of the microstructure, and then determine one or more parameters of the microstructure features. The parameters are compared to thresholds or limits to determine whether remedial action is needed.

Abstract: A template for assigning the most probable root causes for wafer defects. The bin map data for a subject wafer can be compared with bin map data for prior wafers to find wafers with similar issues. A probability can be determined as to whether the same root cause should be applied to the subject wafer, and if so, the wafer can be labeled with that root cause accordingly.

Abstract: A robust predictive model. A plurality of different predictive models for a target feature are run, and a comparative analysis provided for each predictive model that meet minimum performance criteria for the target feature. One of the predictive models is selected, either manually or automatically, based on predefined criteria. For semi-automatic selection, a static or dynamic survey is generated for obtaining user preferences for parameters associated with the target feature. The survey results will be used to generate a model that illustrates parameter trade-offs, which will be used to finalize the optimal predictive model for the user.

Abstract: A template for assigning the most probable root causes for wafer defects. The bin map data for a subject wafer can be compared with bin map data for prior wafers to find wafers with similar issues. A probability can be determined as to whether the same root cause should be applied to the subject wafer, and if so, the wafer can be labeled with that root cause accordingly.

Abstract: Automatic definition of windows for trace analysis. For each process step, the trace data are aligned to both the start of the process step and the end of the process step, and statistics including rate of change are calculated from both the start of the process step and the end of the process step. Windows are generated based on analysis of the calculated statistics.

Abstract: A predictive model for equipment fail modes. An anomaly is detected in a collection of trace data, then key features are calculated. A search is conducted for the same or similar anomalies having the same key features in a database of past trace data. If the same anomaly occurred before and is in the database, then the type of anomaly, its root cause, and action steps to correct can be retrieved from the database.

Abstract: Enhancement of less dominant patterns for parametric wafer measurements. Dominant patterns are removed from the parametric pattern thereby revealing a less dominant pattern. The less dominant patterns can be used to identify root causes for yield loss that are not visible in the original parametric measurements.

Abstract: Scheme for detection and classification of semiconductor equipment faults. Sensor traces are monitored and processed to separate known abnormal operating conditions from unknown abnormal operating conditions. Feature engineering permits focus on relevant traces for a targeted feature. A machine learning model is built to detect and classify based on an initial classification set of anomalies. The machine learning model is continuously updated as more traces are processed and learned.

Abstract: A robust predictive model. A plurality of different predictive models for a target feature are run, and a comparative analysis provided for each predictive model that meet minimum performance criteria for the target feature. One of the predictive models is selected, either manually or automatically, based on predefined criteria. For semi-automatic selection, a static or dynamic survey is generated for obtaining user preferences for parameters associated with the target feature. The survey results will be used to generate a model that illustrates parameter trade-offs, which will be used to finalize the optimal predictive model for the user.

Abstract: Semiconductor yield is modeled at the die level to predict die that are susceptible to early lifetime failure (ELF). A first die yield calculation is made from parametric data obtained from wafer testing in a semiconductor manufacturing process. A second die yield calculation is made from die location only. The difference between the first die yield calculation and the second die yield calculation is a prediction delta. Based on an evaluation of the first die yield calculation and the prediction delta, the likelihood of early lifetime failure can be identified and an acceptable level of die loss can be established to remove die from further processing.

Abstract: A maintenance tool for semiconductor process equipment and components. Sensor data is evaluated by machine learning tools to determine when to schedule maintenance action.

Abstract: Wafer quality is determined by modeling equipment history as a sequence of events, then evaluating anomalous results for individual events. Identifying an event that generates bad wafers narrows the list of possible root causes.