Methods and systems for controlling a semiconductor fabrication process

• US 8,972,029 B2
• Filed: 10/23/2007
• Issued: 03/03/2015
• Est. Priority Date: 11/10/2003
• Status: Active Grant

• Current Assignee: Brooks Automation Ltd. (United Kingdom) (Azenta, Inc.)
• Original Assignee: Brooks Automation Incorporated (Azenta, Inc.)
• Inventors: Pannese, Patrick D., Kavathekar, Vinaya, van der Meulen, Peter
• Primary Examiner(s): Lo, Kenneth
• Assistant Examiner(s): CHANG, SUNRAY
• Application Number:
  US11/877,203
• Publication Number:
  US 20080155446A1
• Time in Patent Office: 2,688 Days
• US Class Current: 700/14
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G05B 19/41865 characterised by job scheduling, process planning, material flow
• CPC Class Codes: G05B 19/41885 characterised by modeling, simulation of the manufacturing system
• CPC Class Codes: G05B 2219/30 Nc systems
• CPC Class Codes: G05B 2219/31 From computer integrated manufacturing till monitoring
• CPC Class Codes: G05B 2219/31467 Display of operating conditions of machines, workcells, selected programs
• CPC Class Codes: G05B 2219/31472 Graphical display of process
• CPC Class Codes: G05B 2219/32128 Gui graphical user interface
• CPC Class Codes: G05B 2219/32267 Dynamic throughput maximization
• CPC Class Codes: G05B 2219/32329 Real time learning scheduler, uses ANN, fuzzy
• CPC Class Codes: G05B 2219/32335 Use of ann, neural network
• CPC Class Codes: G05B 2219/32342 Real time simulation
• CPC Class Codes: G05B 2219/32351 Visual, graphical animation of process
• CPC Class Codes: G05B 2219/35494 Online documentation, manual, procedures, operator, user guidance, assistance
• CPC Class Codes: G05B 2219/40411 Robot assists human in non-industrial environment like home or office
• CPC Class Codes: G05B 2219/40412 Sensor knowledge command fusion network, data and feature and action and constraint
• CPC Class Codes: G05B 2219/40413 Robot has multisensors surrounding operator, to understand intention of operator
• CPC Class Codes: G05B 2219/40419 Task, motion planning of objects in contact, task level programming, not robot level
• CPC Class Codes: G05B 2219/40424 Online motion planning, in real time, use vision to detect workspace changes
• CPC Class Codes: G05B 2219/45031 Manufacturing semiconductor wafers
• CPC Class Codes: G05B 23/0267 : Fault communication, e.g. human machine interface [HMI]
• CPC Class Codes: G05B 99/00 : Subject matter not provided for in other groups of this subclass
• CPC Class Codes: G06F 15/00 : Digital computers in general details G06F1/00 – G06F13/00; Data processing equipment in general
• CPC Class Codes: G06T 7/0004 : Industrial image inspection
• CPC Class Codes: H01L 21/67161 : characterized by the layout of the process chambers
• CPC Class Codes: H01L 21/67184 : characterized by the presence of more than one transfer chamber
• CPC Class Codes: H01L 21/6719 : characterized by the construction of the processing chambers, e.g. modular processing chambers
• CPC Class Codes: H01L 21/67196 : characterized by the construction of the transfer chamber
• CPC Class Codes: H01L 21/67201 : characterized by the construction of the load-lock chamber
• CPC Class Codes: H01L 21/67207 : comprising a chamber adapted to a particular process
• CPC Class Codes: H01L 21/67742 : Mechanical parts of transfer devices robots in general in B25J
• CPC Class Codes: H01L 21/67745 : characterized by movements or sequence of movements of transfer devices
• 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

Software for controlling processes in a heterogeneous semiconductor manufacturing environment may include a wafer-centric database, a real-time scheduler using a neural network, and a graphical user interface displaying simulated operation of the system. These features may be employed alone or in combination to offer improved usability and computational efficiency for real time control and monitoring of a semiconductor manufacturing process. More generally, these techniques may be usefully employed in a variety of real time control systems, particularly systems requiring complex scheduling decisions or heterogeneous systems constructed of hardware from numerous independent vendors.

Claims

1. A computer program product comprising computer executable code embodied on a non-transitory computer readable medium that, when executing on one or more computing devices, performs the steps of:

creating a data structure for a workpiece in a memory of the one or more computing devices, the data structure including an identity of the workpiece and one or more fields for storing workpiece processing data;

receiving the workpiece processing data from a semiconductor manufacturing system that includes a plurality of interconnected process modules that provide workpiece processing data for storage in the data structure while the workpiece is processed by the semiconductor manufacturing system and updating the data structure to provide substantially real time workpiece processing data in the data structure, wherein the workpiece processing is controlled by a neural network and a finite state machine scheduler; and

storing the received workpiece processing data in one of the one or more fields of the data structure where the neural network and the finite state machine scheduler adjust throughput of the semiconductor manufacturing system where the neural network is responsive to inputs that include the workpiece processing data and is configured to generate outputs so that, in combination with the finite state machine scheduler, throughput of the semiconductor manufacturing system is adjusted substantially in real time based on the workpiece processing data.

2. The computer program product of claim 1, wherein the data structure is an object oriented data structure.

3. The computer program product of claim 1, wherein the data structure is embodied in a relational database.

4. The computer program product of claim 1, wherein the code further performs the step of creating a plurality of data structures for a plurality of workpieces.

5. The computer program product of claim 1, wherein the workpiece processing data includes a measured property of the workpiece.

6. The computer program product of claim 5, wherein the measured property includes at least one of a location of the workpiece, a process time of the workpiece, a temperature of the workpiece.

7. The computer program product of claim 1, wherein the workpiece processing data includes an estimated temperature of the workpiece.

8. The computer program product of claim 7, further comprising code that performs the step of updating the estimated temperature according to a thermal model for the workpiece.

9. The computer program product of claim 7, further comprising code that performs the step of storing a time of attaining the estimated temperature.

10. The computer program product of claim 1, wherein the workpiece includes a semiconductor wafer.

11. The computer program product of claim 1, wherein the semiconductor manufacturing system includes a robotic semiconductor wafer handler that provides workpiece processing data for storage in the data structure.

12. The computer program product of claim 1, further comprising code that performs the step of associating a time with the workpiece processing data in one of the one or more fields of the data structure.

13. The computer program product of claim 1, further comprising code that performs the step of storing an attribute of the workpiece processing data in one of the one or more fields of the data structure.

14. The computer program product of claim 13, wherein the attribute identifies at least one of a source of the workpiece processing data, and a time that the workpiece processing data was acquired.

15. The computer program product of claim 1, further comprising code that performs the step of retrieving workpiece processing data from at least one of the one or more fields of the data structure and using the retrieved workpiece processing data to control processing of the workpiece.

16. The computer program product of claim 1 wherein the workpiece processing data includes at least one of a recipe for processing the workpiece, a particle map for the workpiece that identifies a location of one or more particles on the workpiece, and a process history for the workpiece.

17. A method comprising:

creating a data structure for a workpiece in a memory of a device, the data structure including an identity of the workpiece and one or more fields for storing workpiece processing data;

receiving, through a processor of the device, the workpiece processing data from a semiconductor manufacturing system including a plurality of interconnected process modules that provide the workpiece processing data for storage in the data structure while the workpiece is processed by the semiconductor manufacturing system and updating the data structure to provide substantially real time workpiece processing data in the data structure, wherein the workpiece processing is controlled by a neural network and a finite state machine scheduler; and

storing the workpiece processing data in one of the one or more fields of the data structure where the neural network and the finite state machine scheduler adjust throughput of the semiconductor manufacturing system where the neural network is responsive to inputs that include the workpiece processing data and is configured to generate outputs so that, in combination with the finite state machine scheduler, throughput of the semiconductor manufacturing system is adjusted substantially in real time based on the workpiece processing data.

18. The method of claim 17, wherein the workpiece processing data includes a measured property of the workpiece.