An information processing apparatus includes an acquisition unit configured to acquire process information about a substrate process, the process information including process data and a process condition, and a display control unit configured to control a display on a display apparatus based on the process information acquired by the acquisition unit, wherein the display control unit selectively displays, on the display apparatus, a first screen that displays the process data of a lot including a plurality of substrates on a lot-by-lot basis and a second screen that displays the process data of a first lot on a substrate-by-substrate basis, the first lot being a lot designated by a user from the lot displayed on the first screen.

An inspection system may receive inspection datasets from a defect inspection system associated with inspection of one or more samples, where an inspection dataset of the plurality of inspection datasets associated with a defect includes values of two or more signal attributes and values of one or more context attributes. An inspection system may further label each of the inspection datasets with a class label based on respective positions of each of the inspection datasets in a signal space defined by the two or more signal attributes, where each class label corresponds to a region of the signal space. An inspection system may further segment the inspection datasets into two or more defect groups by training a classifier with the values of the context attributes and corresponding class labels for the inspection datasets, where the two or more defect groups are identified based on the trained classifier.

An advanced process control system including a first process tool, a second process tool, and a measurement tool is provided. The first processing tool is configured to process each of a plurality of wafers by one of a plurality of first masks, and provide a first process timing data. The second processing tool is configured to process the wafer processing by the first process tool by one of a plurality of second masks to provide a plurality of works. The second process tool provides a measurement trigger signal according to the first process timing data. The measuring tool is configured to determine whether to perform a measuring operation on each works in response to the measurement trigger signal, and correspondingly provide a measurement result.

A substrate processing apparatus includes a process station for processing a substrate; a cassette station integrated with the process station; a substrate carriage equipped for transferring the substrate between said process station and the cassette station through a passage located at an interface between the process station and said cassette station; and a substrate scanner equipped at said interface between the process station and the cassette station for capturing surface image data during transportation of the substrate that passes through the passage.

A method for monitoring a process variation index includes operations of: obtaining a target parameter to be monitored and a reference parameter used to increase goodness of fit among structural parameters predicted by measuring a structure in a specific location of a wafer; obtaining a reference parameter set in a reference model; and calculating a process variation index capable of confirming a structural change of the structure according to a change in process conditions using the structural parameter and the reference parameter.

The presently-disclosed technology enables real-time inspection of a multitude of subcomponents of a component in parallel. For example, the component may be a semiconductor package, and the subcomponents may include through-silicon vias. One embodiment relates to a method for inspecting multiple subcomponents of a component for defects, the method comprising, for each subcomponent undergoing defect detection: extracting a subcomponent image from image data of the component; computing a transformed feature vector from the subcomponent image; computing pairwise distances from the transformed feature vector to each transformed feature vector in a training set; determining a proximity metric using said pairwise distances; and comparing the proximity metric against a proximity threshold to detect a defect in the subcomponent. Another embodiment relates to a product manufactured using a disclosed method of inspecting multiple subcomponents of a component for defects. Other embodiments, aspects and features are also disclosed.

According to one embodiment, a processing information management system includes: an abnormality analyzer configured to generate abnormality occurrence data of a target wafer based on processing location information, the processing location information collected based on a first sensor outputting a first sensor signal according to a detected processing state, the first sensor provided in a wafer processing apparatus; and an integration system configured to integrate the abnormality occurrence data into wafer map data corresponding to the target wafer.

The inventive concept provides a substrate treating apparatus. The substrate treating apparatus includes a plurality of treating chambers performing a respective treatment on a substrate therein; a transfer chamber having a robot transferring the substrate between the plurality of treating chambers; a detection unit mounted on the robot and configured to detect a substrate state; and a controller for controlling the detection unit, wherein the detection unit comprises: an imaging member for imaging the substrate; and a driving member for moving the imaging member, and wherein the controller controls the detection unit to image and store an image of the substrate at an optimal position and determines whether an image of the substrate is a normal state based on the image obtained in the optimal position, the optimal position determined based on a process variable of the treating chamber.

A method includes receiving first metrology data associated with first substrates produced by manufacturing equipment. The method further includes training a first machine learning model with data input including the first metrology data to generate a first trained machine learning model. The first trained machine learning model is capable of reducing dimensionality of second metrology data associated with second substrates produced by second manufacturing equipment to perform corrective actions associated with the second manufacturing equipment.

Systems and methods of optimizing wafer transport and metrology measurements in a fab are provided. Methods comprise deriving and updating dynamic sampling plans that provide wafer-specific measurement sites and conditions, deriving optimized wafer measurement paths for metrology measurements of the wafers that correspond to the dynamic sampling plan, managing FOUP (Front Opening Unified Pod) transport through the fab, transporting wafers to measurement tools while providing the dynamic sampling plans and the wafer measurement paths to the respective measurement tools before or as the FOUPs with the respective wafers are transported thereto, and carrying out metrology and/or inspection measurements of the respective wafers by the respective measurement tools according to the derived wafer measurement paths. Quantum computing resources may be used to solve the corresponding specific optimization problems, to reduce the required time, improve the calculated solutions and improve the fab yield and accuracy of the produced wafers.