Nanotronics Imaging, Inc.

A computing system generates a training data set for training a machine learning model to detect line defects on a surface of a specimen. The computing system trains the machine learning model to detect line defects based on the training data set. The machine learning model is trained to detect line defects by detecting the individual defects represented by the plurality of annotations extending between the starting point and the end point. The computing system determines that the machine learning model has exceeded a threshold level of accuracy. Based on the determining, the computing system deploys the machine learning model to detect line defects on future specimens.

A computing system receives one or more process parameters to be optimized during a multi-step manufacturing process. The computing system initiates a process prediction model in accordance with the one or more process parameters. The computing system simulates the multi-step manufacturing process using a plurality of sets of different setpoints until the one or more process parameters are optimized. The computing system identifies a first set of setpoints from the plurality of sets of different setpoints that optimized the one or more process parameters. The computing system causes the station controller to apply the first set of setpoints to the one or more stations.

Various embodiments relate to a method for analyzing manufacturing process data. The method includes: receiving, by a processor, a sequence of sensor outputs from a plurality of sensors monitoring a manufacturing process; predicting, using a transformer model executed by the processor, future manufacturing process parameters based on the sensor outputs; generating one or more key influencers on a current system state based on an attention matrix of the transformer model; analyzing the predicted parameters to identify an out-of-specification parameter; and identifying one or more key contributors to the out-of-specification parameter based on the attention matrix of a transformer head associated therewith.

Systems, methods, and media for additive manufacturing are provided. In some embodiments, an additive manufacturing system comprises: a hardware processor that is configured to: receive a captured image; apply a trained failure classifier to a low-resolution version of the captured image; determine that a non-recoverable failure is not present in the printed layer of the object; generate a cropped version of the low-resolution version of the captured image; apply a trained binary error classifier to the cropped version of the low-resolution version of the captured image; determine that an error is present in the printed layer of the object; apply a trained extrusion classifier to the captured image, wherein the trained extrusion classifier generates an extrusion quality score; and adjust a value of a parameter of the print head based on the extrusion quality score to print a subsequent layer of the printed object.

Systems and methods of analyzing aligning a golden template with a scanned image of specimen under manufacture are disclosed herein. A computing system generates a golden template for a patterned specimen design by forming a single coherent image from a first plurality of scanned images of a plurality of specimens manufactured according to the patterned specimen design. The computing system identifies a plurality of regions of interest in the patterned specimen design that is present across a second plurality of scanned images of the plurality of specimens manufactured according to the patterned specimen design. The computing system receives a new scanned image of a new specimen manufactured in accordance with the patterned specimen design. The computing system aligns the golden template with the new scanned image of the new specimen by performing a pixel-by-pixel analysis in the plurality of regions of interest only.

A computer-implemented method for detecting defects on specimens in an inspection system is disclosed herein. A first set of images of a plurality of specimens having defects formed thereon is received. A second set of images of the plurality of specimens is received, the second set of images includes the plurality of specimens after undergoing a destructive etch process and labels corresponding to each defect. Labels from the second set of images are transferred to the first set of images. A machine learning model is trained to classify defects on unetched specimens based on the first set of images and the labeled first set of images. Once the machine learning model has achieved a threshold of accuracy, the machine learning model may be deployed in the inspection system.

A roofing system for renewable energy applications and a method for installing the same are disclosed herein. The roofing system includes a roofing sub-structure, a plurality of openings, a channel, and a plurality of filler panels. The plurality of openings is formed in the roofing sub-structure. The channel is formed in the roofing sub-structure. The channel is configured to receive wiring. The plurality of filler is panels attached to the roofing sub-structure via the plurality of openings. The plurality of filler panels provides standard places for the addition of solar panels to the roofing system.

A manufacturing system is disclosed herein. The manufacturing system includes a monitoring platform and an analytics platform. The monitoring platform is configured to capture data of an operator during assembly of an article of manufacture. The monitoring platform includes one or more cameras and one or more microphones. The analytics platform is in communication with the monitoring platform. The analytics platform is configured to analyze the data captured by the monitoring platform.

An imaging system is disclosed herein. The imaging system includes an imaging apparatus and a computing system. The imaging apparatus includes a plurality of light sources positioned at a plurality of positions and a plurality of angles relative to a stage configured to support a specimen. The imaging apparatus is configured to capture a plurality of images of a surface of the specimen. The computing system in communication with the imaging apparatus. The computing system configured to generate a 3D-reconstruction of the surface of the specimen by receiving, from the imaging apparatus, the plurality of images of the surface of the specimen, generating, by the imaging apparatus via a deep learning model, a height map of the surface of the specimen based on the plurality of images, and outputting a 3D-reconstruction of the surface of the specimen based on the height map generated by the deep learning model.

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.