Abstract: Methods, systems, and non-transitory computer readable medium are provided for long short-term memory (LSTM) anomaly detection for multi-sensor equipment monitoring. A method includes training a LSTM recurrent neural network (RNN) model for semiconductor processing fault detection. The training includes generating training data for the LSTM RNN model and providing the training data to train the LSTM RNN model on first training input and first target output to generate a trained LSTM RNN model for the semiconductor processing fault detection. The training data includes the first training input and the first target output based on normal runs of manufacturing processes of semiconductor processing equipment. Another method includes providing input based on runs of manufacturing processes of semiconductor processing equipment to a trained LSTM RNN model; obtaining one or more outputs from the trained LSTM RNN model; and using the one or more outputs for semiconductor processing fault detection.

Abstract: The system and method of the invention pertains to an axial flux stator is implemented to replace the drive-end magnets and the drive motor. The axial flux stator comprises a control circuit to control the voltage and current provided to the stator, to measure the torque and speed of rotation, and to measure the magnetic flux and magnetic flux density produced by the axial flux stator and impeller magnets, individually or in combination. The axial flux stator comprises a plurality of current carrying elements to produce magnetic flux in an axial direction and drive the impeller.

Abstract: A method includes determining, based on sensor data, that one or more components of substrate processing equipment are within a pre-failure window that is after a normal operation window. Corresponding data points in the normal operation window are substantially stable along a first health index value. The corresponding data points in the pre-failure window increase from the first health index value to a peak at a second health index value. Responsive to the determining that the one or more components are within the pre-failure window, the method further includes causing performance of a corrective action associated with the one or more components of the substrate processing equipment.

Abstract: Training of a machine vision model, a segmentation model, is performed by using an acquisition function for a small number of pixels of one or more training images. The acquisition function uses first mutual information and second mutual information to identify unlabelled pixels which are labelled with high uncertainty when predicting possible label values. Training, prediction of labels, identifying pixels with highly uncertain labels, obtaining labels only for those pixels with highly uncertain labels and retraining are performed iteratively to finally provide the machine vision model. The iterative approach uses very few labelled pixels to obtain the final machine vision model. The machine vision model accurately labels areas of a data image.

Abstract: A problem of supervised learning is overcome by using patches to discover objects in unlabeled training images. The discovered objects are embedded in a pattern space. An AI machine replaces manual entry steps of training with a machine-centric process including clustering in a pixel space, clustering in latent space and building the pattern space based on different losses derived from pixel space clustering and latent space clustering. A distance structure in the pattern space captures the co-occurrence of patterns due to frequently appearing objects in training image data. Embodiments provide image representation based on local image patch naturally handles the position and scale invariance property that is important to effective object detection. Embodiments successfully identifies frequent objects such as human faces, human bodies, animals, or vehicles from unorganized data images based on a small quantity of training images.

Abstract: A method includes determining, based on sensor data, that one or more components of substrate processing equipment are within a pre-failure window that is after a normal operation window. Corresponding data points in the normal operation window are substantially stable along a first health index value. The corresponding data points in the pre-failure window increase from the first health index value to a peak at a second health index value. Responsive to the determining that the one or more components are within the pre-failure window, the method further includes causing performance of a corrective action associated with the one or more components of the substrate processing equipment.

Abstract: Methods, systems, and non-transitory computer readable medium are provided for correcting component failures in ion implant semiconductor manufacturing tool. A method includes receiving, from sensors associated with an ion implant tool, current sensor data corresponding to features; performing feature analysis to generate additional features for the current sensor data; providing the additional features as input to a trained machine learning model; obtaining one or more outputs from the trained machine learning model, where the one or more outputs are indicative of a level of confidence of a predicted window; predicting, based on the level of confidence of the predicted window, whether one or more components of the ion implant tool are within a pre-failure window; and responsive to predicting that the one or more components are within the pre-failure window, performing a corrective action associated with the ion implant tool.

Abstract: A problem of imbalanced big data is solved by decoupling a classifier into a neural network for generation of representation vectors and into a classification model for operating on the representation vectors. The neural network and the classification model act as a mapper classifier. The neural network is trained with an unsupervised algorithm and the classification model is trained with a supervised active learning loop. An acquisition function is used in the supervised active learning loop to speed arrival at an accurate classification performance, improving data efficiency. The accuracy of the hybrid classifier is similar to or exceeds the accuracy of comparative classifiers in all aspects. In some embodiments, big data includes an imbalance of more than 10:1 in image classes. The hybrid classifier reduces labor and improves efficiency needed to arrive at an accurate classification performance, and improves recognition of previously-unrecognized images.

Abstract: A server trains a neural network by feeding a first set of input time-series data of one or more sensors of a first processing chamber that is within specification to the neural network to produce a corresponding first set of output time-series data. The server calculates a first error. The server feeds a second set of input time-series data from corresponding one or more sensors associated with a second processing chamber under test to the trained neural network to produce a corresponding second set of output time-series data. The server calculates a second error.

Abstract: The system and method of the invention pertains to an axial flux stator is implemented to replace the drive-end magnets and the drive motor. The axial flux stator comprises a control circuit to control the voltage and current provided to the stator, to measure the torque and speed of rotation, and to measure the magnetic flux and magnetic flux density produced by the axial flux stator and impeller magnets, individually or in combination. The axial flux stator comprises a plurality of current carrying elements to produce magnetic flux in an axial direction and drive the impeller.

Abstract: The system and method of the invention pertains to an axial flux stator is implemented to replace the drive-end magnets and the drive motor. The axial flux stator comprises a control circuit to control the voltage and current provided to the stator, to measure the torque and speed of rotation, and to measure the magnetic flux and magnetic flux density produced by the axial flux stator and impeller magnets, individually or in combination. The axial flux stator comprises a plurality of current carrying elements to produce magnetic flux in an axial direction and drive the impeller.

Abstract: Methods, systems, and non-transitory computer readable medium are provided for correcting component failures in ion implant semiconductor manufacturing tool. A method includes receiving, from sensors associated with an ion implant tool, current sensor data corresponding to features; performing feature analysis to generate additional features for the current sensor data; providing the additional features as input to a trained machine learning model; obtaining one or more outputs from the trained machine learning model, where the one or more outputs are indicative of a level of confidence of a predicted window; predicting, based on the level of confidence of the predicted window, whether one or more components of the ion implant tool are within a pre-failure window; and responsive to predicting that the one or more components are within the pre-failure window, performing a corrective action associated with the ion implant tool.

Abstract: A server trains a neural network by feeding a first set of input time-series data of one or more sensors of a first processing chamber that is within specification to the neural network to produce a corresponding first set of output time-series data. The server calculates a first error. The server feeds a second set of input time-series data from corresponding one or more sensors associated with a second processing chamber under test to the trained neural network to produce a corresponding second set of output time-series data. The server calculates a second error.

Abstract: Embodiments of the method disclosed regard use of a torque sensor (e.g., transducer) and using the measured torque to detect the different fluid and mixing properties, conditions, and abnormalities in a mixing process. The torque produced in the mixing process relates to different fluid properties such as viscosity and density. It also relates to different mixing conditions such as presence of obstacles and changes or issues with gas sparging. Moreover, torque measurements enable determination of power transmitted to fluid by actual measurement, in contrast to using solely empirical impeller power number and speed, and allowing for actual mass transfer determination (i.e., gas transfer calculations).

Abstract: Methods, systems, and non-transitory computer readable medium are provided for long short-term memory (LSTM) anomaly detection for multi-sensor equipment monitoring. A method includes training a LSTM recurrent neural network (RNN) model for semiconductor processing fault detection. The training includes generating training data for the LSTM RNN model and providing the training data to train the LSTM RNN model on first training input and first target output to generate a trained LSTM RNN model for the semiconductor processing fault detection. The training data includes the first training input and the first target output based on normal runs of manufacturing processes of semiconductor processing equipment. Another method includes providing input based on runs of manufacturing processes of semiconductor processing equipment to a trained LSTM RNN model; obtaining one or more outputs from the trained LSTM RNN model; and using the one or more outputs for semiconductor processing fault detection.

Abstract: The system and method of the invention pertains to an axial flux stator is implemented to replace the drive-end magnets and the drive motor. The axial flux stator comprises a control circuit to control the voltage and current provided to the stator, to measure the torque and speed of rotation, and to measure the magnetic flux and magnetic flux density produced by the axial flux stator and impeller magnets, individually or in combination. The axial flux stator comprises a plurality of current carrying elements to produce magnetic flux in an axial direction and drive the impeller.

Abstract: The system and method of the invention pertains to use of a back iron on one or both ends of the impeller to increase the magnetic field density, and thus strengthen the magnetic coupling. In addition, pie-shaped (i.e. wedge) magnets, or variations thereof, increase the utilization volume and hence provide higher torque to allow the use of less expensive material (e.g. ferrites). In another embodiment, the rotor side is constructed with a Halbach array which increases the torque without the need to add a back iron piece. In another embodiment, an axial flux stator is implemented to replace the drive-end magnets and the drive motor.

Abstract: Embodiments of the method disclosed regard use of a torque sensor (e.g., transducer) and using the measured torque to detect the different fluid and mixing properties, conditions, and abnormalities in a mixing process. The torque produced in the mixing process relates to different fluid properties such as viscosity and density. It also relates to different mixing conditions such as presence of obstacles and changes or issues with gas sparging. Moreover, torque measurements enable determination of power transmitted to fluid by actual measurement, in contrast to using solely empirical impeller power number and speed, and allowing for actual mass transfer determination (i.e., gas transfer calculations).

Abstract: The system and method of the invention pertains to use of a back iron on one or both ends of the impeller to increase the magnetic field density, and thus strengthen the magnetic coupling. In addition, pie-shaped (i.e. wedge) magnets, or variations thereof, increase the utilization volume and hence provide higher torque to allow the use of less expensive material (e.g. ferrites). In another embodiment, the rotor side is constructed with a Halbach array which increases the torque without the need to add a back iron piece. In another embodiment, an axial flux stator is implemented to replace the drive-end magnets and the drive motor.