Abstract: Real-time quality control of a culture for bioproduction is facilitated using machine learning. In this approach, real-time process data for a set of parameters for a current production run is received. Based on this process data, a prediction is made using an instance of a machine learning model that has been trained on process data from past production or development runs. The instance is uniquely associated to a particular culture day and thus independent of any other instance of the machine learning model (for other culture days). Based on the prediction, a quality control recommendation for the current production run is then made. Several different types of predictions are enabled, and various different recommendations are provided based on the predictions.

Abstract: The subject matter herein provides for AI-based prediction of production defects in association with a production system, such as a semiconductor manufacturing machine. In one embodiment, a method begins by receiving production data from the production system. The production data typically comprises non-homogeneous machine parameters and maintenance data, quality test data, and product and process data. Using the production data, a neural network is trained to model an operation of a given machine in the production system. Preferably, the training involves multi-task learning, transfer learning (e.g., using knowledge obtained with respect to a machine of the same type as the given machine), and a combination of multi-task learning and transfer learning. Once the model is trained, it is associated with the given machine operating environment, wherein it is used to provide quality assurance predictions.

Abstract: Real-time quality control of a culture for bioproduction is facilitated using machine learning. In this approach, real-time process data for a set of parameters for a current production run is received. Based on this process data, a prediction is made using an instance of a machine learning model that has been trained on process data from past production or development runs. The instance is uniquely associated to a particular culture day and thus independent of any other instance of the machine learning model (for other culture days). Based on the prediction, a quality control recommendation for the current production run is then made. Several different types of predictions are enabled, and various different recommendations are provided based on the predictions.

Abstract: The subject matter herein provides for AI-based prediction of production defects in association with a production system, such as a semiconductor manufacturing machine. In one embodiment, a method begins by receiving production data from the production system. The production data typically comprises non-homogeneous machine parameters and maintenance data, quality test data, and product and process data. Using the production data, a neural network is trained to model an operation of a given machine in the production system. Preferably, the training involves multi-task learning, transfer learning (e.g., using knowledge obtained with respect to a machine of the same type as the given machine), and a combination of multi-task learning and transfer learning. Once the model is trained, it is associated with the given machine operating environment, wherein it is used to provide quality assurance predictions.

Abstract: Real-time quality control of a culture for bioproduction is facilitated using machine learning. In this approach, real-time process data for a set of parameters for a current production run is received. Based on this process data, a prediction is made using an instance of a machine learning model that has been trained on process data from past production or development runs. The instance is uniquely associated to a particular culture day and thus independent of any other instance of the machine learning model (for other culture days). Based on the prediction, a quality control recommendation for the current production run is then made. Several different types of predictions are enabled, and various different recommendations are provided based on the predictions.

Abstract: Real-time quality control of a culture for bioproduction is facilitated using machine learning. In this approach, real-time process data for a set of parameters for a current production run is received. Based on this process data, a prediction is made using an instance of a machine learning model that has been trained on process data from past production or development runs. The instance is uniquely associated to a particular culture day and thus independent of any other instance of the machine learning model (for other culture days). Based on the prediction, a quality control recommendation for the current production run is then made. Several different types of predictions are enabled, and various different recommendations are provided based on the predictions.

Abstract: Real-time quality control of a culture for bioproduction is facilitated using machine learning. In this approach, real-time process data for a set of parameters for a current production run is received. Based on this process data, a prediction is made using an instance of a machine learning model that has been trained on process data from past production or development runs. The instance is uniquely associated to a particular culture day and thus independent of any other instance of the machine learning model (for other culture days). Based on the prediction, a quality control recommendation for the current production run is then made. Several different types of predictions are enabled, and various different recommendations are provided based on the predictions.

Abstract: The subject matter herein provides for AI-based prediction of production defects in association with a production system, such as a semiconductor manufacturing machine. In one embodiment, a method begins by receiving production data from the production system. The production data typically comprises non-homogeneous machine parameters and maintenance data, quality test data, and product and process data. Using the production data, a neural network is trained to model an operation of a given machine in the production system. Preferably, the training involves multi-task learning, transfer learning (e.g., using knowledge obtained with respect to a machine of the same type as the given machine), and a combination of multi-task learning and transfer learning. Once the model is trained, it is associated with the given machine operating environment, wherein it is used to provide quality assurance predictions.