METHOD, STORAGE MEDIUM AND SYSTEM FOR CONTROLLING THE PROCESSING OF LOTS OF WORKPIECES

• US 20150162180A1
• Filed: 12/09/2013
• Published: 06/11/2015
• Est. Priority Date: 12/09/2013
• Status: Active Grant

• Current Assignee: Globalfoundries US Incorporated (GlobalFoundries, Inc.)
• Original Assignee: GlobalFoundries, Inc.
• Inventors: Weigang, Joerg, Fosnight, William John, Oza, Chinmay, Grau, Gero, Kalisch, Steffen, Pabst, Detlef
• Granted Patent: US 9,748,088 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G05B 17/02 electric
• CPC Class Codes: G05B 19/0426 Programming the control sequence
• CPC Class Codes: G05B 19/41875 characterised by quality surveillance of production
• CPC Class Codes: G05B 2219/23006 Finite state modeling
• CPC Class Codes: H01L 21/02 Manufacture or treatment of semiconductor devices or of parts thereof
• CPC Class Codes: H01L 21/67727 using a general scheme of a conveying path within a factory
• CPC Class Codes: Y02P 90/02 Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
• CPC Class Codes: Y02P 90/80 Management or planning

Abstract

A method includes processing each of a plurality of lots with at least one first equipment and moving some of the plurality of lots to a first storage. For each of a plurality of second equipments, an expected dispatch time of one or more next lots for processing by the second equipment is determined Each of the lots in the first storage is assigned to one of the plurality of second equipments on the basis of at least the determined expected dispatch times and moved to one of a plurality of second storages that is associated with one of the plurality of second equipments to which the respective lot was assigned. For each of the plurality of second equipments, each of the lots in the second storage associated with the second equipment is moved to the second equipment and are processed with the second equipment.

Claims

1. A method, comprising:

processing each of a plurality of lots with a respective one of at least one first equipment;

moving at least some of said plurality of lots from said respective one of said at least one first equipment to a first storage;

for each of a plurality of second equipments, determining an expected dispatch time of one or more next lots for processing by said second equipments;

assigning each of the lots in said first storage to one of said plurality of second equipments on the basis of at least the determined expected dispatch time;

moving each of the lots in said first storage to one of a plurality of second storages that is associated with the one of said plurality of second equipments to which the respective lot was assigned; and

for each of said plurality of second equipments, moving each of the lots in said second storage associated with the second equipment to the second equipment and processing the lots from said second storage associated with the second equipment with the second equipment.

15. The method of claim 1, wherein the method further comprises:

providing one or more models, the one or more models relating at least data from at least said plurality of second equipments to the expected dispatch times;

wherein the determination of the expected dispatch times comprises;

receiving data from at least said plurality of second equipments; and

using said one or more models and the received data to determine, for each of said plurality of second equipments, the expected dispatch time.

16. The method of claim 15, further comprising:

for at least a portion of said plurality of second equipments, determining a measure of an accuracy of the expected dispatch time determined for said second equipments; and

updating at least a portion of said one or more models on the basis of the determined measures of accuracy.

17. The method of claim 16, wherein the updating of at least a portion of said one or more models comprises performing a machine learning algorithm.

2. The method of claim 2, wherein each of said plurality of lots comprises a plurality of semiconductor wafers, wherein each of said at least one first equipment performs one or more first steps of a semiconductor manufacturing process for each wafer of each of said plurality of lots that is processed with the respective first equipment, and wherein each of said plurality of second equipments performs one or more second steps of the semiconductor manufacturing process for each wafer of each of said plurality of lots that is processed with the respective second equipment.

3. The method of claim 2, wherein at least one of said at least one first equipment and said plurality of second equipments comprises a plurality of tools.

4. The method of claim 3, wherein said plurality of tools comprises a first tool and a second tool that successively perform different steps of the semiconductor manufacturing process.

5. The method of claim 3, wherein moving each of said lots from the respective one of said at least one first equipment to said first storage and moving each of said lots in said first storage to one of said plurality of second storages that is associated with said one of the plurality of second equipments to which the respective lot was assigned are performed by an automated material handling system.

6. The method of claim 5, wherein said first storage is closer to each of said at least one first equipment than any of said plurality of second storages.

7. The method of claim 5, wherein each of said plurality of second storages is closer to the one of the plurality of second equipments associated with the second storage than the first storage.

8. The method of claim 7, wherein a storage space of said first storage is greater than a storage space of any of said plurality of second storages.

9. The method of claim 8, wherein a storage space of each of said plurality of second storages is at least one of less than about one-half of the storage space of said first storage, less than about one-fifth of the storage space of said first storage and in a range from about one-tenth to about one-fifth of the storage space of said first storage.

10. The method of claim 8, wherein the storage space of each of said plurality of second storages is sufficient to store a number of lots that is at least one of equal to and greater than a maximum number of lots that can be processed by the second equipment associated with the second storage during a predetermined delay buffer time.

11. The method of claim 9, wherein said assigning each of the lots in said first storage to one of said plurality of second equipments is additionally performed on the basis of a priority of each of the lots, a number of lots present in each of said plurality of second storages, and a status of each of said second storages.

12. The method of claim 11, wherein each of the lots in said first storage is assigned to one of said plurality of second equipments after a point in time of an insertion of the respective lot into said first storage.

13. The method of claim 12, further comprising determining a decision time at which at least one of the lots in said first storage is to be assigned to one of said plurality of second equipments at least on the basis of the determined expected dispatch time, wherein the assigning of each of the lots in said first storage to one of said plurality of second equipments is performed at the determined decision time.

14. The method of claim 13, wherein the expected dispatch times are determined on the basis of expected times of availability of the second equipments, estimated transport times between the first storage and each of the plurality of second storages and estimated transport times between the plurality of second storages and the respective ones of the plurality of second equipments associated therewith.

18. A method, comprising:

providing a first model, said first model relating at least data from a tool to an expected dispatch time of one or more next lots for processing by the tool;

receiving at least said data from said tool;

using said first model and said received data to determine the expected dispatch time;

assigning one or more first lots of workpieces to said tool on the basis of said determined expected dispatch time, moving said one or more first lots of workpieces to said tool and processing said one or more first lots of workpieces by means of said tool;

determining a measure of accuracy of said determined expected dispatch time; and

updating said first model on the basis of said determined measure of accuracy.

19. The method of claim 18, wherein the updating of said first model comprises performing a machine learning algorithm.

20. The method of claim 19, wherein each of said workpieces of said one or more first lots comprises a semiconductor wafer, wherein said tool is a semiconductor tool, and wherein the processing of said workpieces of said one or more first lots comprises performing one or more steps of a process for forming semiconductor devices.

21. The method of claim 20, wherein said tool comprises a plurality of stations, and wherein said data from said tool comprises at least one of:

a time taken by each of a plurality of wafers of one or more second lots to move from at least one carrier to a first one of said plurality of stations;

a time taken by each of said wafers of said one or more second lots to move from a last one of said plurality of stations back to said at least one carrier;

a time taken by each of said wafers of said one or more second lots at each of said plurality of stations;

a number of wafers currently in said tool;

a processing time per wafer of said one or more second lots related to process steps;

a time taken by each of said at least one carrier to perform at least one of docking said carrier, undocking said carrier, scanning a wafer map of said carrier, receiving a process recipe for one of the processing of said one or more second lots associated with the carrier, and creating a job for processing said one or more second lots;

a time taken by each of said at least one carrier to complete after a last one of said wafers of said one or more second lots associated with said carrier has returned to said carrier after processing;

information relating to correlations between wafers processed by said tool at different times;

historic states of said tool comprising at least one of a standby time, a productive time and a time since a last preventive maintenance;

one or more paths of said wafers of said one or more second lots through said plurality of stations of said tool related to a process recipe.

22. The method of claim 21, wherein said tool comprises a plurality of processing chambers, and wherein said data from said tool comprises a distribution of wafer loading over said plurality of chambers.

23. The method of claim 21, wherein said tool has a storage associated therewith, and wherein said data received from said tool further comprise states of one or more stored lots in said storage.

24. The method of claim 21, wherein said first model additionally relates data from an automated material handling system to the expected dispatch time.

25. The method of claim 24, wherein said data from said automated material handling system comprises at least one of:

a time taken by said automated material handling system to move said at least one carrier from a first location to a second location;

a time taken by said automated material handling system to pick up said at least one carrier; and

a time taken by said automated material handling system to deliver said at least one carrier.

26. The method of claim 21, wherein said first model comprises a data mining model.

27. The method of claim 21, wherein said first model comprises a state machine model.

28. The method of claim 21, wherein said first model comprises a combination of a data mining model and a state machine model.

29. The method of claim 28, further comprising:

determining if a decision criterion is fulfilled;

determining the expected dispatch time by means of said state machine model if said decision criterion is fulfilled; and

determining the expected dispatch time by means of said data mining model if said decision criterion is not fulfilled.

30. The method of claim 24, further comprising:

providing a second model, said second model relating at least the data from said tool to an expected pickup time at which a processing of a plurality of work-in-process workpieces at said tool is completed;

using said second model and the data received from said tool to determine an expected pickup time; and

operating said automated material handling system for removing said work-in-process workpieces from said tool on the basis of the determined expected pickup time.

31. The method of claim 24, further comprising maintaining a queue comprising a plurality of lots of workpieces to be processed, said plurality of lots of workpieces comprising said one or more first lots of workpieces, and wherein the assigning of said one or more first lots of workpieces to said tool further comprises reordering the queue.

32. The method of claim 18, wherein each of said one or more first lots of workpieces is provided in at least one carrier when said one or more first lots of workpieces are moved to said tool, wherein two or more lots of workpieces are provided in each of said at least one carrier.

33. A system, comprising:

a data processing arrangement, at least one first equipment, a plurality of second equipments, an automated material handling system, a first storage and a plurality of second storages, each of said plurality of second equipments being associated with a respective one of said plurality of second storages;

wherein said automated material handling system moves at least some of a plurality of lots from a respective one of said at least one first equipment to said first storage;

wherein the data processing arrangement determines, for each of said plurality of second equipments, an expected dispatch time of one or more next lots for processing by said second equipments, assigns each of said lots in said first storage to one of said plurality of second equipments on the basis of at least said expected dispatch times and sends one or more first signals and one or more second signals to said automated material handling system;

wherein said automated material handling system, in response to said one or more first signals from said data processing arrangement, moves each of said lots in said first storage to said one of said plurality of second storages that is associated with said one of said plurality of second equipments to which the respective lot was assigned;

wherein said automated material handling system, in response to said one or more second signals from said data processing arrangement, moves each of said lots in each of said plurality of second storages to said one of said plurality of second equipments associated with the respective second storage; and

wherein each of said plurality of second equipments processes the lots received from said automated material handling system.

34. A system, comprising:

means for processing each of a plurality of lots with a respective one of at least one first equipment;

means for moving at least some of said plurality of lots from the respective one of said at least one first equipment to a first storage;

means for determining, for each of a plurality of second equipments, an expected dispatch time of one or more next lots for processing by said second equipments;

means for assigning each of said lots in said first storage to one of said plurality of second equipments on the basis of at least the determined expected dispatch times;

means for moving each of said lots in said first storage to one of a plurality of second storages that is associated with the one of said plurality of second equipments to which the respective lot was assigned; and

means for moving, for each of said plurality of second equipments, each of said lots in said second storage associated with said second equipments to said second equipments and processing said lots from said second storage associated with said second equipments with said second equipment.

35. A computer-readable storage medium comprising code for causing a data processing arrangement to:

provide a model, said model relating at least data from a tool to an expected dispatch time of one or more next lots for processing by said tool;

receive at least said data from said tool through a network connection;

use said model and said received data to determine the expected dispatch time;

assign one or more lots of workpieces to said tool on the basis of said determined expected dispatch time;

send a first signal to an automated material handling system through said network connection, said first signal causing said automated material handling system to move said one or more lots of workpieces to said tool;

send a second signal to said tool through said network connection, said second signal causing said tool to process said one or more lots of workpieces;

determine a measure of accuracy of said determined expected dispatch time; and

update said model on the basis of said determined measure of accuracy.