Abstract: A computing system identifies a trajectory example generated by a human operator. The trajectory example includes trajectory information of the human operator while performing a task to be learned by a control system of the computing system. Based on the trajectory example, the computing system trains the control system to perform the task exemplified in the trajectory example. Training the control system includes generating an output trajectory of a robot performing the task. The computing system identifies an updated trajectory example generated by the human operator based on the trajectory example and the output trajectory of the robot performing the task. Based on the updated trajectory example, the computing system continues to train the control system to perform the task exemplified in the updated trajectory example.

Abstract: A deep learning process receives desired process values associated with the one or more process stations. The deep learning processor receives desired target values for one or more key performance indicators of the manufacturing process. The deep learning processor simulates the manufacturing process to generate expected process values and expected target values for the one or more key performance indicators to optimize the one or more key performance indicators. The simulating includes generating a proposed state change of at least one processing parameter of the initial set of processing parameters. The deep learning processor determines that expected process values and the expected target values are within an acceptable limit of the desired process values and the desired target values. Based on the determining, the deep learning processes causes a change to the initial set of processing parameters based on the proposed state change.

Abstract: A manufacturing system is disclosed herein. The manufacturing system includes one or more stations, a monitoring platform, and a control module. Each station of the one or more stations is configured to perform at least one step in a multi-step manufacturing process for a component. The monitoring platform is configured to monitor progression of the component throughout the multi-step manufacturing process. The control module is configured to dynamically adjust processing parameters of each step of the multi-step manufacturing process to achieve a desired final quality metric for the component.

Abstract: A manufacturing system is disclosed herein. The manufacturing system includes one or more stations, a monitoring platform, and a control module. Each station of the one or more stations is configured to perform at least one step in a multi-step manufacturing process for a component. The monitoring platform is configured to monitor progression of the component throughout the multi-step manufacturing process. The control module is configured to detect a cyberattack to the manufacturing system. The control module is configured to perform operations. The operations include receiving control values for a first station of the one or more stations. The operations further include determining that there is a cyberattack based on the control values for the first station using one or more machine learning algorithms. The operations further include generating an alert to cease processing of the component. In some embodiments, the operations further include correcting errors caused by the cyberattack.

Abstract: A manufacturing system is disclosed herein. The manufacturing system includes one or more stations, a monitoring platform, and a control module. Each station of the one or more stations is configured to perform at least one step in a multi-step manufacturing process for a component. The monitoring platform is configured to monitor progression of the component throughout the multi-step manufacturing process. The control module is configured to dynamically adjust processing parameters of each step of the multi-step manufacturing process to achieve a desired final quality metric for the component.

Abstract: A system and method for generating a training data set for training a machine learning model to detect defects in specimens is described herein. A computing system cause presentation of an image on a device of a user. The image includes at least one defect on an example specimen. The computing system receives an annotated image from the user. The user annotated the image using an input via the device. The input includes a first indication of a location of the defect and a second indication of a class corresponding to the defect. The computing system adjusts the annotated image to standardize the input based on an error profile of the user and the class corresponding to the defect. The computing system uploads the annotated image for training the machine learning model.

Abstract: A controller emulator, coupled to an interface that exposes the controller emulator to inputs from external sources, provides one or more control signals to a process simulator and a deep learning process. In response, the process simulator simulates response data that is provided to the deep learning processor. The deep learning processor generates expected response data and expected behavioral pattern data for the one or more control signals, as well as actual behavioral pattern data for the simulated response data. A comparison of at least one of the simulated response data to the expected response data and the actual behavioral pattern data to the expected behavioral pattern data is performed to determine whether anomalous activity is detected. As a result of detecting anomalous activity, one or more operations are performed to address the anomalous activity.

Abstract: A computing system generates a training data set for training the prediction model to detect defects present in a target surface of a target specimen and training the prediction model to detect defects present in the target surface of the target specimen based on the training data set. The computing system generates the training data set by identifying a set of images for training the prediction model, the set of images comprising a first subset of images. A deep learning network generates a second subset of images for subsequent labelling based on the set of images comprising the first subset of images. The deep learning network generates a third subset of images for labelling based on the set of images comprising the first subset of images and the labeled second subset of images. The computing system continues the process until a threshold number of labeled images is generated.

Abstract: A method and system for mapping fluid objects on a substrate using a microscope inspection system that includes a light source, imaging device, stage for moving a substrate disposed on the stage, and a control module. A computer analysis system includes an object identification module that identifies for each of the objects on the substrate, an object position on the substrate including a set of X, Y, and ? coordinates using algorithms, networks, machines and systems including artificial intelligence and image processing algorithms. At least one of the objects is fluid and has shifted from a prior position or deformed from a prior size.

Abstract: A manufacturing system is disclosed herein. The manufacturing system includes one or more stations, a monitoring platform, and a control module. Each station of the one or more stations is configured to perform at least one step in a multi-step manufacturing process for a component. The monitoring platform is configured to monitor progression of the component throughout the multi-step manufacturing process. The control module is configured to dynamically adjust processing parameters of each step of the multi-step manufacturing process to achieve a desired final quality metric for the component.

Abstract: A computing system receives, from an inspection device, an image of a multi-device substrate having one or more regions. Each region of the one or more regions having a plurality of devices formed thereon. The computing system identifies a golden die corresponding to the multi-device substrate. The golden die represents a canonical design of devices formed on the multi-device substrate. The computing system analyzes the image to identify defects in the image of the multi-device substrate by comparing the image of the multi-device substrate to the golden die. The computing system maps locations of the identified defects to corresponding locations on the golden die. The computing system, based on the mapping, determines, by the computing system, one or more devices of the plurality of devices affected by the defects. The computing system generates, as output, an indication of the defects and the one or more devices affected by the defects.

Abstract: Aspects of the disclosed technology encompass the use of a deep learning controller for monitoring and improving a manufacturing process. In some aspects, a method of the disclosed technology includes steps for: receiving a plurality of control values from two or more stations, at a deep learning controller, wherein the control values are generated at the two or more stations deployed in a manufacturing process, predicting an expected value for an intermediate or final output of an article of manufacture, based on the control values, and determining if the predicted expected value for the article of manufacture is in-specification. In some aspects, the process can further include steps for generating control inputs if the predicted expected value for the article of manufacture is not in-specification. Systems and computer-readable media are also provided.

Abstract: Aspects of the disclosed technology encompass the use of a deep-learning controller for monitoring and improving a manufacturing process. In some aspects, a method of the disclosed technology includes steps for: receiving control values associated with a process station in a manufacturing process, predicting an expected value for an article of manufacture output from the process station, and determining if the deep-learning controller can control the manufacturing process based on the expected value. Systems and computer-readable media are also provided.

Abstract: A manufacturing system is disclosed herein. The manufacturing system includes one or more stations, a monitoring platform, and a control module. Each station of the one or more stations is configured to perform at least one step in a multi-step manufacturing process for a component. The monitoring platform is configured to monitor progression of the component throughout the multi-step manufacturing process. The control module is configured to detect a cyberattack to the manufacturing system. The control module is configured to perform operations. The operations include receiving control values for a first station of the one or more stations. The operations further include determining that there is a cyberattack based on the control values for the first station using one or more machine learning algorithms. The operations further include generating an alert to cease processing of the component. In some embodiments, the operations further include correcting errors caused by the cyberattack.

Abstract: A system including a deep learning processor obtains response data of at least two data types from a set of process stations performing operations as part of a manufacturing process. The system analyzes factory operation and control data to generate expected behavioral pattern data. Further, the system uses the response data to generate actual behavior pattern data for the process stations. Based on an analysis of the actual behavior pattern data in relation to the expected behavioral pattern data, the system determines whether anomalous activity has occurred as a result of the manufacturing process. If it is determined that anomalous activity has occurred, the system provides an indication of this anomalous activity.

Abstract: A manufacturing system is disclosed herein. The manufacturing system includes one or more stations, a monitoring platform, and a control module. Each station of the one or more stations is configured to perform at least one step in a multi-step manufacturing process for a component. The monitoring platform is configured to monitor progression of the component throughout the multi-step manufacturing process. The control module is configured to dynamically adjust processing parameters of each step of the multi-step manufacturing process to achieve a desired final quality metric for the component.

Abstract: A computing system identifies a trajectory example generated by a human operator. The trajectory example includes trajectory information of the human operator while performing a task to be learned by a control system of the computing system. Based on the trajectory example, the computing system trains the control system to perform the task exemplified in the trajectory example. Training the control system includes generating an output trajectory of a robot performing the task. The computing system identifies an updated trajectory example generated by the human operator based on the trajectory example and the output trajectory of the robot performing the task. Based on the updated trajectory example, the computing system continues to train the control system to perform the task exemplified in the updated trajectory example.

Abstract: A manufacturing system is disclosed herein. The manufacturing system includes one or more stations, a monitoring platform, and a control module. Each station of the one or more stations is configured to perform at least one step in a multi-step manufacturing process for a component. The monitoring platform is configured to monitor progression of the component throughout the multi-step manufacturing process. The control module is configured to dynamically adjust processing parameters of each step of the multi-step manufacturing process to achieve a desired final quality metric for the component.

Abstract: A deep learning process receives desired process values associated with the one or more process stations. The deep learning processor receives desired target values for one or more key performance indicators of the manufacturing process. The deep learning processor simulates the manufacturing process to generate expected process values and expected target values for the one or more key performance indicators to optimize the one or more key performance indicators. The simulating includes generating a proposed state change of at least one processing parameter of the initial set of processing parameters. The deep learning processor determines that expected process values and the expected target values are within an acceptable limit of the desired process values and the desired target values. Based on the determining, the deep learning processes causes a change to the initial set of processing parameters based on the proposed state change.

Abstract: A microscopy system and method of focusing the same are disclosed herein. The microscopy system may include an objective, and imaging device, an illumination source, an epi-illumination module, and a controller. The imaging device is configured to capture a single image of a specimen positioned on a stage of the microscopy system. The illumination source is configured to illuminate the specimen positioned on the stage. The epi-illumination module includes a focusing mechanism in a first primary optical path of a light generated by the illumination source. The focusing mechanism is tilted in relation to a plane perpendicular to the first primary optical path. The controller is in communication with the illumination source. The controller is configured to focus the microscopy system based on a pattern produced by the focusing mechanism on the single image captured by the imaging device.