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: Systems, devices, and methods for performing a non-contact electrical measurement (NCEM) on a NCEM-enabled cell included in a NCEM-enabled cell vehicle may be configured to perform NCEMs while the NCEM-enabled cell vehicle is moving. The movement may be due to vibrations in the system and/or movement of a movable stage on which the NCEM-enabled cell vehicle is positioned. Position information for an electron beam column producing the electron beam performing the NCEMs and/or for the moving stage may be used to align the electron beam with targets on the NCEM-enabled cell vehicle while it is moving.

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: Systems, devices, and methods for performing a non-contact electrical measurement (NCEM) on a NCEM-enabled cell included in a NCEM-enabled cell vehicle may be configured to perform NCEMs while the NCEM-enabled cell vehicle is moving. The movement may be due to vibrations in the system and/or movement of a movable stage on which the NCEM-enabled cell vehicle is positioned. Position information for an electron beam column producing the electron beam performing the NCEMs and/or for the moving stage may be used to align the electron beam with targets on the NCEM-enabled cell vehicle while it is moving.

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.

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: 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: 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 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 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: An IC that includes a contiguous standard cell area with a 4x3 e-beam pad that is compatible with advanced manufacturing processes and an associated e-beam testable structure.

Abstract: An IC that includes a contiguous standard cell area with a 4×3 e-beam pad that is compatible with advanced manufacturing processes and an associated e-beam testable structure.

Abstract: An IC that includes a contiguous standard cell area with a 4×3 e-beam pad that is compatible with advanced manufacturing processes and an associated e-beam testable structure.

Abstract: An IC that includes a contiguous standard cell area with a 4×3 e-beam pad that is compatible with advanced manufacturing processes and an associated e-beam testable structure.

Abstract: A model is generated for predicting failures at the wafer production level. Input data from sensors is stored as an initial dataset, then data exhibiting excursions or useless impact is removed from the dataset. The dataset is converted into target features, where the target features are useful in predicting whether a wafer will be normal or not. A trade-off between positive and negative results is selected, and a plurality of predictive models are created. The final model is selected based on the trade-off criteria, and deployed.

Abstract: Robust machine learning predictions. Temporal dependencies of process targets for different machine learning models can be captured and evaluated for the impact on process performance for target. The most robust of these different models is selected for deployment based on minimizing variance for the desired performance characteristic.

Abstract: A method for predicting yield for a semiconductor process. A particular type of wafer is fabricated to have a first set of features disposed on the wafer, with a wafer map identifying a location for each of the first set of features on the wafer. Data from wafer acceptance tests and circuit probe tests is collected over time for wafers of that particular type as made in a semiconductor fabrication process, and at least one training dataset and a least one validation dataset are created from the collected data. A second set of “engineered” features are created and also incorporated onto the wafer and wafer map. Important features from the first and second sets of features are identified and selected, and using those important features as inputs, a number of different process models are run, with yield as the target. The results of the different models can be combined, for example, statistically.