Abstract: A fluorescence microscopy inspection system includes light sources able to emit light that causes a specimen to fluoresce and light that does not cause a specimen to fluoresce. The emitted light is directed through one or more filters and objective channels towards a specimen. A ring of lights projects light at the specimen at an oblique angle through a darkfield channel. One of the filters may modify the light to match a predetermined bandgap energy associated with the specimen and another filter may filter wavelengths of light reflected from the specimen and to a camera. The camera may produce an image from the received light and specimen classification and feature analysis may be performed on the image.

Abstract: An inspection apparatus includes a specimen stage configured to retain a specimen, at least three imaging devices arranged in a triangular array positioned above the specimen stage, each of the at least three imaging devices configured to capture an image of the specimen, one or more sets of lights positioned between the specimen stage and the at least three imaging devices, and a control system in communication with the at least three imaging devices.

Abstract: A training set that includes at least two data types corresponding to operations and control of a manufacturing process is obtained. A deep learning processor is trained to predict expected characteristics of output control signals that correspond with one or more corresponding input operating instructions. A first input operating instruction is received from a first signal splitter. A first output control signal is received from a second signal splitter. The deep learning processor correlates the first input operating instruction and the first output control signal. Based on the correlating, the deep learning processor determines that the first output control signal is not within a range of expected values based on the first input operating instruction. Responsive to the determining, an indication of an anomalous activity is provided as a result of detection of the anomalous activity in the manufacturing process.

Abstract: A system is disclosed herein. The system includes a splitter board. The splitter board includes a microprocessor, a converter, and a bypass relay. The converter includes analog-to-digital circuitry and digital-to-analog circuitry. The bypass relay is configurable between a first state and a second state. In the first state, the bypass relay is configured to direct an input signal to the converter. The converter converts the input signal to a converted input signal and splits the converted input signal into a first portion and a second portion. The first portion is directed to the microprocessor. The second portion is directed to an output port of the splitter board for downstream processes. In the second state, the bypass relay is configured to cause the input signal to bypass the converter. The bypass relay directs the input signal to the output port of the splitter board for the downstream processes.

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: Systems, methods, and computer-readable media for feedback on and improving the accuracy of super-resolution imaging. In some embodiments, a low resolution image of a specimen can be obtained using a low resolution objective of a microscopy inspection system. A super-resolution image of at least a portion of the specimen can be generated from the low resolution image of the specimen using a super-resolution image simulation. Subsequently, an accuracy assessment of the super-resolution image can be identified based on one or more degrees of equivalence between the super-resolution image and one or more actually scanned high resolution images of at least a portion of one or more related specimens identified using a simulated image classifier. Based on the accuracy assessment of the super-resolution image, it can be determined whether to further process the super-resolution image. The super-resolution image can be further processed if it is determined to further process the super-resolution image.

Abstract: A simulated process is initiated. The simulated process includes generating, by an emulator, a control signal based on external inputs. The simulated process further includes processing, by a simulator, the control signal to generate simulated response data. The simulated process further includes generating, by a deep learning processor, expected behavioral pattern data based on the simulated response data. An actual process is initiated by initializing setpoints for a process station in a manufacturing system. The actual process includes generating, by the deep learning processor, actual behavioral pattern data based on actual process data from the at least one process station. The deep learning processor compares the expected behavioral pattern to the actual behavioral pattern. Based on the comparing, the deep learning processor determines that anomalous activity is present in the manufacturing system. Based on the anomalous activity being present, the deep learning processor initiates an alert protocol.

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 system is disclosed herein. The system includes a splitter board. The splitter board includes a microprocessor, a converter, and a bypass relay. The converter includes analog-to-digital circuitry and digital-to-analog circuitry. The bypass relay is configurable between a first state and a second state. In the first state, the bypass relay is configured to direct an input signal to the converter. The converter converts the input signal to a converted input signal and splits the converted input signal into a first portion and a second portion. The first portion is directed to the microprocessor. The second portion is directed to an output port of the splitter board for downstream processes. In the second state, the bypass relay is configured to cause the input signal to bypass the converter. The bypass relay directs the input signal to the output port of the splitter board for the downstream processes.

Abstract: A method for altering the intensity of light across the field of view of an image sensor in a microscope apparatus having a light source, an image sensor having pixels, and a specimen stage, wherein light from the light source travels along a light path to the specimen stage and then to the image sensor includes interposing a programmable spatial light modulator, pSLM, in the light path between the light source and the image sensor, the pSLM having a plurality of pixels; and modulating the intensity of light passing through one or more pixels of the plurality of pixels of the pSLM to produce an altered illumination landscape at the field of view of the image sensor that differs from an unaltered illumination landscape that would otherwise be produced at the image sensor. Vignetting can be specifically addressed.

Abstract: A training set that includes at least two data types corresponding to operations and control of a manufacturing process is obtained. A deep learning processor is trained to predict expected characteristics of output control signals that correspond with one or more corresponding input operating instructions. A first input operating instruction is received from a first signal splitter. A first output control signal is received from a second signal splitter. The deep learning processor correlates the first input operating instruction and the first output control signal. Based on the correlating, the deep learning processor determines that the first output control signal is not within a range of expected values based on the first input operating instruction. Responsive to the determining, an indication of an anomalous activity is provided as a result of detection of the anomalous activity in the manufacturing process.

Abstract: Systems, methods, and computer-readable media for feedback on and improving the accuracy of super-resolution imaging. In some embodiments, a low resolution image of a specimen can be obtained using a low resolution objective of a microscopy inspection system. A super-resolution image of at least a portion of the specimen can be generated from the low resolution image of the specimen using a super-resolution image simulation. Subsequently, an accuracy assessment of the super-resolution image can be identified based on one or more degrees of equivalence between the super-resolution image and one or more actually scanned high resolution images of at least a portion of one or more related specimens identified using a simulated image classifier. Based on the accuracy assessment of the super-resolution image, it can be determined whether to further process the super-resolution image. The super-resolution image can be further processed if it is determined to further process the super-resolution image.

Abstract: A process is provided for imaging a surface of a specimen with an imaging system that employs a BD objective having a darkfield channel and a bright field channel, the BD objective having a circumference. The specimen is obliquely illuminated through the darkfield channel with a first arced illuminating light that obliquely illuminates the specimen through a first arc of the circumference. The first arced illuminating light reflecting off of the surface of the specimen is recorded as a first image of the specimen from the first arced illuminating light reflecting off the surface of the specimen, and a processor generates a 3D topography of the specimen by processing the first image through a topographical imaging technique. Imaging apparatus is also provided as are further process steps for other embodiments.

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 system is disclosed herein. The system includes a splitter board. The splitter board includes a microprocessor, a converter, and a bypass relay. The converter includes analog-to-digital circuitry and digital-to-analog circuitry. The bypass relay is configurable between a first state and a second state. In the first state, the bypass relay is configured to direct an input signal to the converter. The converter converts the input signal to a converted input signal and splits the converted input signal into a first portion and a second portion. The first portion is directed to the microprocessor. The second portion is directed to an output port of the splitter board for downstream processes. In the second state, the bypass relay is configured to cause the input signal to bypass the converter. The bypass relay directs the input signal to the output port of the splitter board for the downstream processes.

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 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 system including a deep learning processor receives one or more control signals from one or more of a factory's process, equipment and control (P/E/C) systems during a manufacturing process. The processor generates expected response data and expected behavioral pattern data for the control signals. The processor receives production response data from the one or more of the factory's P/E/C systems and generates production behavioral pattern data for the production response data. The process compares at least one of: the production response data to the expected response data, and the production behavioral pattern data to the expected behavioral pattern data to detect anomalous activity. As a result of detecting anomalous activity, the processor performs one or more operations to provide notice or cause one or more of the factory's P/E/C systems to address the anomalous activity.

Abstract: A system is disclosed herein. The system includes a splitter board. The splitter board includes a microprocessor, a converter, and a bypass relay. The converter includes analog-to-digital circuitry and digital-to-analog circuitry. The bypass relay is configurable between a first state and a second state. In the first state, the bypass relay is configured to direct an input signal to the converter. The converter converts the input signal to a converted input signal and splits the converted input signal into a first portion and a second portion. The first portion is directed to the microprocessor. The second portion is directed to an output port of the splitter board for downstream processes. In the second state, the bypass relay is configured to cause the input signal to bypass the converter. The bypass relay directs the input signal to the output port of the splitter board for the downstream processes.

Abstract: A positive airway pressure device includes a blower, a chamber, a sensor, and a controller. The controller is configured to perform operations. The operations include determining a baseline respiratory response for a patient. The operations further include initializing the blower to deliver a therapy pressure to the patient. The operations further include receiving, from the sensor, real-time respiratory response data while delivering therapy to the patient. The operations further include analyzing the real-time respiratory response data to determine whether a sleep disruption has occurred by comparing the real-time respiratory response data to the baseline respiratory response for the patient. The operations further include, based on the analyzing, determining that a sleep disruption has occurred based on an anomaly detected in the real-time respiratory response data. The operations further include, based on the determining, initiating an action to account for the sleep disruption.