Method and system for mapping objects on unknown specimens
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 θ 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.
1. A method, comprising:
generating, by a computing system, a prediction model for predicting coordinates of an object on a substrate at each stage of processing by:
generating a training data set comprising labeled examples of known objects on substrates and labeled examples of deformed objects on substrates, and
training the prediction model to detect objects on the substrates and predict coordinates of each object at each stage of processing;
receiving, by the computing system, a scan of a set of target objects positioned on a stage of a microscope system;
identifying, by the computing system, a position of each target object in the set of target objects; and
predicting, by the computing system via the prediction model, coordinates of each target object in the set of target objects at different stages of examination.
2. The method of claim 1 , wherein predicting, by the computing system, the coordinates of each target object in the set of target objects at different stages of examination comprises:
predicting a second set of coordinates of each target object at a downstream process.
3. The method of claim 1 , further comprising:
comparing, by the computing system, the predicted coordinates of each target object in the set of target objects to the identified position of each target object in the set of target objects; and
generating, by the computing system, an alert upon determining that the predicted coordinates of each target object exceeds a threshold tolerance from the identified position of each target object.
4. The method of claim 1 , further comprising:
assessing, by the computing system, a degree of deformity of each target object based on the predicting.
5. The method of claim 4 , further comprising:
identifying, by the computing system, an actual degree of deformity of each target object following a downstream processing stage; and
assessing, by the computing system, an accuracy of the prediction model based on a comparison between the actual degree of deformity and the degree of deformity.
6. The method of claim 1 , further comprising:
generating, by the computing system, object mapping information based on the predicting.
7. The method of claim 6 , further comprising:
generating, by the computing system, an object layout map using the object mapping information.
8. A microscope inspection system, comprising:
one or more processors in communication with a microscope system that includes a stage for moving a set of objects disposed on the stage and an imaging device, wherein the imaging device scans the set of objects from the stage; and
a memory having programming instructions stored thereon, which, when executed by the one or more processors, performs one or more operations, comprising:
generating a prediction model for predicting coordinates of an object on a substrate at each stage of processing by:
generating a training data set comprising labeled examples of known objects on substrates and labeled examples of deformed objects on substrates, and
training the prediction model to detect objects on the substrates and predict coordinates of each object at each stage of processing;
receiving a scan of a set of target objects positioned on the stage of the microscope system;
identifying a position of each target object in the set of target objects; and
predicting, by the prediction model, coordinates of each target object in the set of target objects at different stages of examination.
9. The microscope inspection system of claim 8 , wherein predicting, by the prediction model, the coordinates of each target object in the set of target objects at different stages of examination comprises:
predicting a second set of coordinates of each target object at a downstream process.
10. The microscope inspection system of claim 8 , wherein the one or more operations further comprise:
comparing the predicted coordinates of each target object in the set of target objects to the identified position of each target object in the set of target objects; and
generating an alert upon determining that the predicted coordinates of each target object exceeds a threshold tolerance from the identified position of each target object.
11. The microscope inspection system of claim 8 , wherein the one or more operations further comprise:
assessing a degree of deformity of each target object based on the predicting.
12. The microscope inspection system of claim 11 , wherein the one or more operations further comprise:
identifying an actual degree of deformity of each target object following a downstream processing stage; and
assessing an accuracy of the prediction model based on a comparison between the actual degree of deformity and the degree of deformity.
13. The microscope inspection system of claim 8 , wherein the one or more operations further comprise:
generating object mapping information based on the predicting.
14. The microscope inspection system of claim 13 , wherein the one or more operations further comprise:
generating an object layout map using the object mapping information.
15. A method, comprising:
training, by a computing system, a prediction model to predict coordinates of an object on a substrate at a downstream processing stage based on detected positions of objects on the substrate at an upstream processing stage;
receiving, by the computing system, a scan of a set of target objects positioned on a stage of a microscope system;
identifying, by the computing system, a position of each target object in the set of target objects; and
predicting, by the computing system via the prediction model, coordinates of each target object in the set of target objects at different stages of examination.
16. The method of claim 15 , further comprising:
comparing, by the computing system, the predicted coordinates of each target object in the set of target objects to the identified position of each target object in the set of target objects; and
generating, by the computing system, an alert upon determining that the predicted coordinates of each target object exceeds a threshold tolerance from the identified position of each target object.
17. The method of claim 15 , further comprising:
assessing, by the computing system, a degree of deformity of each target object based on the predicting.
18. The method of claim 17 , further comprising:
identifying, by the computing system, an actual degree of deformity of each target object following a downstream processing stage; and
assessing, by the computing system, an accuracy of the prediction model based on a comparison between the actual degree of deformity and the degree of deformity.
19. The method of claim 15 , further comprising:
generating, by the computing system, object mapping information based on the predicting.
20. The method of claim 19 , further comprising:
generating, by the computing system, an object layout map using the object mapping information.