What is claimed is:
1. A laser marking apparatus for automatically marking semiconductor chips at a marking location comprising:
at least one chip feeder for automatically feeding individual semiconductor chips to said marking location, said chip feeder having at least one magazine;
a laser having a laser beam laser beam mounted adjacent said at least one chip feeder, said laser beam aligned and directed toward a chip after feeding said individual semiconductor chip by said chip feeder to said marking location of said laser marking apparatus, said at least one laser comprising one type laser of a Nd:YAG laser, a Nd:YLF laser, and a carbon dioxide laser;
a laser reactive material applicator mounted adjacent said marking location of said laser marking apparatus, said applicator for applying a laser reactive material to a surface of said individual semiconductor chips for producing a mark thereon upon contact by said laser beam, said laser beam of said at least one laser contacting said laser reactive material applied to said surface of said individual semiconductor chips for producing said mark on said surface of said individual semiconductor chips.
2. The laser marking apparatus of claim 1, wherein said at least one chip feeder includes at least two magazines.
3. The laser marking apparatus of claim 1, wherein said at least one chip feeder includes at least one conveyor mechanism.
4. The laser marking apparatus of claim 1, wherein said at least one chip feeder includes at least one carrier containing at least one chip.
5. The laser marking apparatus of claim 4, wherein said at least one chip has at least two exposed surfaces when disposed in said at least one carrier.
6. The laser marking apparatus of claim 1, wherein said at least one chip feeder is gravity fed.
7. The laser marking apparatus of claim 6, further including at least one automated indexing pin for holding said individual semiconductor chips in position against gravity.
8. The laser marking apparatus of claim 5, wherein said at least one chip feeder includes a track.
9. The laser marking apparatus of claim 8, wherein said at least one carrier is oriented in an inverted position on said track.
10. The laser marking apparatus of claim 8, wherein said at least one laser is oriented in an inverted position relative to said track.
11. The laser marking apparatus of claim 1, wherein said at least one laser is movable in at least one direction.
12. The laser marking apparatus of claim 11, wherein said laser reactive material applicator is movable in conjunction with said at least one laser.
13. The laser marking apparatus of claim 1, wherein said laser reactive material contains a pigment.
14. The laser marking apparatus of claim 1, wherein said laser reactive material changes color when exposed to said laser beam.
15. The laser marking apparatus of claim 1, wherein said laser reactive material is an epoxy.
16. The laser marking apparatus of claim 1, wherein said laser reactive material is a resin.
17. The laser marking apparatus of claim 1, wherein said laser reactive material is heat absorbent.
18. The laser marking apparatus of claim 1, wherein said laser reactive material is a ribbon of ink bearing material.
19. The laser marking apparatus of claim 1, further including a CPU for controlling said laser marking apparatus.
20. The laser marking apparatus of claim 19, wherein said CPU controls operation and movement of said laser beam.
21. The laser marking apparatus of claim 19, wherein said CPU controls said laser reactive material applicator.
22. The laser marking apparatus of claim 19, further including a camera downstream of said at least one laser for photographing said individual semiconductor chips.
23. The laser marking apparatus of claim 22, wherein said CPU receives input from said camera for controlling the quality of said mark on said individual semiconductor chips.
24. The laser marking apparatus of claim 1, wherein said laser reactive material includes one or more ribbons of ink bearing material.
25. The laser marking apparatus of claim 1, wherein said laser reactive material includes a plurality of ribbons of ink bearing material.
26. The laser marking apparatus of claim 25, wherein said laser reactive material including a plurality of ribbons of ink bearing material having at least two different colors of ink.
27. An energy marking apparatus for automatically marking semiconductor chips at a marking location comprising:
at least one chip feeder for automatically feeding an individual semiconductor chip to said marking location, said chip feeder including at least one magazine;
at least one energy source having an energy beam mounted adjacent said at least one chip feeder, said energy beam aligned and directed toward a chip associated with said at least one chip feeder located at said marking location;
an energy reactive material applicator mounted adjacent said marking location, said energy reactive material applicator for applying an energy reactive material to a surface of said individual semiconductor chip for producing a mark thereon upon contact by said energy beam; and
a CPU for controlling said at least one energy source.
28. The energy marking apparatus of claim 27, wherein said at least one energy source is a light source.
29. The energy marking apparatus of claim 27, wherein said at least one energy source is a heat source.
30. The energy marking apparatus of claim 27, wherein said energy reactive material is light absorbent.
31. The energy marking apparatus of claim 27, wherein said energy reactive material changes color when exposed to said energy beam.
32. The laser marking apparatus of claim 27, wherein said CPU controls operation of the energy marking apparatus.
33. The energy marking apparatus of claim 27, wherein said energy reactive material applicator is mounted to said at least one energy source so that movement of said at least one energy source results in movement of said energy reactive material applicator.
34. The laser marking apparatus of claim 27, wherein said energy reactive material applicator is a ribbon of ink bearing material that extends over said surface of said individual semiconductor chips.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A computer-implemented method comprising:
receiving, in a system of one or more computers, a set of training data for predictive modeling;
determining, by the system, one or more attributes of the training data;
identifying, by the system in a mapping of attributes to types of predictive models, a type of predictive model that is mapped to at least one of the one or more attributes;
obtaining a utility function that specifies a first weighted value to be applied to a first item of training data in the set and that further specifies a second, different weighted value to be applied to a second, different item of training data in the set, with a weighted value for an item of training data specifying an importance of the item of training data, relative to another importance of another item of training data;
assigning, based on the utility function, (i) the first weighted value to the first item of training data in the set, and (ii) the second, different weighted value to the second, different item of training data in the set; and
training, based on assigning the first and second weighted values, a predictive model of the identified type.
2. (canceled)
3. The computer-implemented method of claim 1, wherein training comprises:
determining one or more patterns in the training data.
4. The computer-implemented method of claim 1, wherein the identified type of predictive model comprises one or more of a k-nearest neighbor predictive model, a logistic regression predictive model, a Naive Bayes predictive model, and a support vector predictive model.
5. The computer-implemented method of claim 1, wherein the one or more attributes comprise one or more of a size of the training data, an estimate of an amount of time required to train a predictive model with the training data, a number of categories in the training data, and a number of features of the training data.
6. The computer-implemented method of claim 1, wherein the predictive model of the identified type comprises a k-nearest neighbor predictive model, and wherein the method further comprises:
receiving input data;
identifying, from the k-nearest neighbor predictive model, a k-nearest item of data that is closest to the input data, relative to closeness of other k-nearest items of data to the input data; and
assigning to the input data a category associated with the identified k-nearest item data.
7. A system comprising:
one or more computers; and
one or more storage devices storing instructions that are executable by the one or more computers to perform operations comprising:
receiving a set of training data for predictive modeling;
determining one or more attributes of the training data;
identifying, in a mapping of attributes to types of predictive models, a type of predictive model that is mapped to at least one of the one or more attributes;
obtaining a utility function that specifies a first weighted value to be applied to a first item of training data in the set and that further specifies a second, different weighted value to be applied to a second, different item of training data in the set, with a weighted value for an item of training data specifying an importance of the item of training data, relative to another importance of another item of training data;
assigning, based on the utility function, (i) the first weighted value to the first item of training data in the set, and (ii) the second, different weighted value to the second, different item of training data in the set; and
training, based on assigning the first and second weighted values, a predictive model of the identified type.
8. (canceled)
9. The system of claim 7, wherein training comprises:
determining one or more patterns in the training data.
10. The system of claim 7, wherein the identified type of predictive model comprises one or more of a k-nearest neighbor predictive model, a logistic regression predictive model, a Naive Bayes predictive model, and a support vector predictive model.
11. The system of claim 7, wherein the one or more attributes comprise one or more of a size of the training data, an estimate of an amount of time required to train a predictive model with the training data, a number of categories in the training data, and a number of features of the training data.
12. The system of claim 7, wherein the predictive model of the identified type comprises a k-nearest neighbor predictive model, and wherein the operations further comprise:
receiving input data;
identifying, from the k-nearest neighbor predictive model, a k-nearest item of data that is closest to the input data, relative to closeness of other k-nearest items of data to the input data; and
assigning to the input data a category associated with the identified k-nearest item data.
13. One or more storage devices storing instructions that are executable by one or more computers to perform operations comprising:
receiving a set of training data for predictive modeling;
determining one or more attributes of the training data;
identifying, in a mapping of attributes to types of predictive models, a type of predictive model that is mapped to at least one of the one or more attributes;
obtaining a utility function that specifies a first weighted value to be applied to a first item of training data in the set and that further specifies a second, different weighted value to be applied to a second, different item of training data in the set, with a weighted value for an item of training data specifying an importance of the item of training data, relative to another importance of another item of training data;
assigning, based on the utility function, (i) the first weighted value to the first item of training data in the set, and (ii) the second, different weighted value to the second, different item of training data in the set; and
training, based on assigning the first and second weighted values, a predictive model of the identified type.
14. (canceled)
15. The one or more storage devices of claim 13, wherein training comprises:
determining one or more patterns in the training data.
16. The one or more storage devices of claim 13, wherein the identified type of predictive model comprises one or more of a k-nearest neighbor predictive model, a logistic regression predictive model, a Naive Bayes predictive model, and a support vector predictive model.
17. The one or more storage devices of claim 13, wherein the one or more attributes comprise one or more of a size of the training data, an estimate of an amount of time required to train a predictive model with the training data, a number of categories in the training data, and a number of features of the training data.
18. The one or more storage devices of claim 13, wherein the predictive model of the identified type comprises a k-nearest neighbor predictive model, and wherein the operations further comprise:
receiving input data;
identifying, from the k-nearest neighbor predictive model, a k-nearest item of data that is closest to the input data, relative to closeness of other k-nearest items of data to the input data; and
assigning to the input data a category associated with the identified k-nearest item data.
19. A system comprising:
means for receiving a set of training data for predictive modeling;
means for determining one or more attributes of the training data;
means for identifying, in a mapping of attributes to types of predictive models, a type of predictive model that is mapped to at least one of the one or more attributes;
means for obtaining a utility function that specifies a first weighted value to be applied to a first item of training data in the set and that further specifies a second, different weighted value to be applied to a second, different item of training data in the set, with a weighted value for an item of training data specifying an importance of the item of training data, relative to another importance of another item of training data;
means for assigning, based on the utility function, (i) the first weighted value to the first item of training data in the set, and (ii) the second, different weighted value to the second, different item of training data in the set; and
means for training, based on assigning the first and second weighted values, a predictive model of the identified type.