Methods and systems for controlling a semiconductor fabrication process

• US 8,639,489 B2
• Filed: 10/23/2007
• Issued: 01/28/2014
• 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): JONES, HUGH M
• Application Number:
  US11/877,096
• Publication Number:
  US 20080155442A1
• Time in Patent Office: 2,289 Days
• Field of Search: 703/6, 703/17
• US Class Current: 703/17
• CPC Class Codes: G05B 19/41885 characterised by modeling, simulation of the manufacturing system
• 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 2219/32335 Use of ann, neural network
• CPC Class Codes: G05B 2219/32342 Real time simulation
• CPC Class Codes: G05B 2219/35494 Online documentation, manual, procedures, operator, user guidance, assistance
• 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 method comprising:

controlling operation of a semiconductor manufacturing system with a neural network to schedule processing of one or more workpieces, the semiconductor manufacturing system including at least two workpiece handling robots in at least two processing planes, the robots capable of handing workpieces between the at least two planes;

creating a data structure for one of the one or more workpieces, the data structure including an identity of the one or more workpieces and one or more fields for storing state information of the one or more workpieces; and

receiving data from the semiconductor manufacturing system as inputs to the neural network;

wherein the neural network is configured to effect modification of a processing task that changes a physical characteristic of a structure of the one or more workpieces in scheduled processes for each individual workpiece based on the state information related to the physical characteristic of the structure of respective ones of the one or more workpieces and the data received from the semiconductor manufacturing system; and

wherein the neural network outputs weights for one or more states of a finite state machine where the one or more states provide control signals for controlling operation of a semiconductor manufacturing system, and the neural network separates processing that reconfigures the state machine from operation of the finite state machine such that the finite state machine is enabled to operate independent of the neural network for allowing the neural network to evaluate outputs where processing time of the neural network extends beyond a time increment of the finite state machine.

2. The method of claim 1, wherein the semiconductor manufacturing system includes a tunnel-based cart workpiece transport facility.

3. The method of claim 2, wherein workpiece transport status associated with the tunnel-based cart transport facility is included in the information relating to the workpiece.

4. The method of claim 1, wherein the semiconductor manufacturing system comprises modularly assembled modules.

5. The method of claim 1, wherein the information relating to the workpiece includes information related to processing of the workpiece.

6. The method of claim 5, wherein processing includes at least one of heating and cooling.

7. The method of claim 5, wherein the information relating to processing includes a reference to data associated with physical device attributes that is stored in a data library for simulation of the physical device, wherein the physical device is included in the semiconductor manufacturing system.

8. The method of claim 1, wherein the information relating to the workpiece includes technical information related to a process associated with the workpiece.

9. The method of claim 1, wherein the information relating to the workpiece includes workpiece edge sensing data.

10. The method of claim 1, wherein data from the semiconductor manufacturing system represents a state of an item of hardware within the semiconductor manufacturing system.

11. The method of claim 1, wherein data from the semiconductor manufacturing system represents a position of a workpiece within the semiconductor manufacturing system.

12. The method of claim 1, wherein data from the semiconductor manufacturing system represents a position of an isolation valve within the system.

13. The method of claim 1, wherein the inputs to the neural network include one or more of sensor data, temperature data, a detected workpiece position, an estimated workpiece temperature, an actual workpiece temperature, a valve state, an isolation valve state, robotic drive encoder data, robotic arm position data, end effector height data, a process time, a process status, a pick time, a place time, and a control signal.

14. The method of claim 1, wherein inputs to the neural network include data from the workpiece data structure.

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

controlling operation of a semiconductor manufacturing system with a neural network to schedule processing of one or more workpieces, the semiconductor manufacturing system including at least two workpiece handling robots in at least two processing planes, the robots capable of handing workpieces between the at least two planes;

creating a data structure for one of the one or more workpieces, the data structure including an identity of the one workpiece and one or more fields for storing state information of the one workpiece;

receiving data from the semiconductor manufacturing system as inputs to the neural network; and

modifying a processing task that changes a physical characteristic of a structure of the one or more workpieces in scheduled processes for each individual workpiece with the neural network based on the state information related to the physical characteristic of the structure of respective ones of the one or more workpieces and the data received from the semiconductor manufacturing system; and

wherein the neural network outputs weights for one or more states of a finite state machine where the one or more states provide control signals for controlling operation of a semiconductor manufacturing system, and the neural network separates processing that reconfigures the state machine from operation of the finite state machine such that the finite state machine is enabled to operate independent of the neural network for allowing the neural network to evaluate outputs where processing time of the neural network extends beyond a time increment of the finite state machine.

16. A system comprising:

a hardware control unit including a neural network connected to and configured to control operation of a semiconductor manufacturing system to schedule processing of one or more workpieces, the semiconductor manufacturing system including at least two workpiece handling robots in at least two processing planes, the robots capable of handing workpieces between the at least two planes, wherein the neural network controller receives data from the semiconductor manufacturing system; and

a hardware memory including a workpiece data structure, the data structure including an identity of one of the one or more workpieces and one or more fields for storing state information of the workpiece;

wherein the neural network is configured to effect modification of a processing task that changes a physical characteristic of a structure of the one or more workpieces in scheduled processes for each individual workpiece based on the state information related to the physical characteristic of the structure of respective ones of the one or more workpieces and the data received from the semiconductor manufacturing system; and

wherein the neural network outputs weights for one or more states of a finite state machine where the one or more states provide control signals for controlling operation of a semiconductor manufacturing system, and the neural network separates processing that reconfigures the state machine from operation of the finite state machine such that the finite state machine is enabled to operate independent of the neural network for allowing the neural network to evaluate outputs where processing time of the neural network extends beyond a time increment of the finite state machine.

17. The system of claim 16, wherein data from the semiconductor manufacturing system represents a state of an item of hardware within the semiconductor manufacturing system.

18. The system of claim 16, wherein data from the semiconductor manufacturing system represents a position of a workpiece within the semiconductor manufacturing system.

19. The system of claim 16, wherein data from the semiconductor manufacturing system represents a position of an isolation valve within the system.