Methods and systems for controlling a semiconductor fabrication process

• US 8,639,365 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): Ali, Mohammad
• Assistant Examiner(s): Booker, Kelvin
• Application Number:
  US11/877,071
• Publication Number:
  US 20080163094A1
• Time in Patent Office: 2,289 Days
• Field of Search: 700/28, 700/48, 700/47, 706 14- 44
• US Class Current: 700/28
• CPC Class Codes: G03F 7/70525 Controlling normal operating mode, e.g. matching different apparatus, remote control or prediction of failure
• CPC Class Codes: G05B 13/042 in which a parameter or coefficient is automatically adjusted to optimise the performance
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 3/0481 based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• CPC Class Codes: G06N 20/00 Machine learning

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 system comprising:

a neural network enabled controller for controlling the operation of a semiconductor manufacturing system to schedule processing of one or more workpieces, wherein the neural network receives as inputs data from the semiconductor manufacturing system, where the data from the semiconductor manufacturing system embodies a position identification of an isolation valve within the system through which workpieces pass and that selectively isolates an environment of process tool from a shared vacuum environment of the system that transfers the workpieces to the process tool; and

a workpiece data structure, the data structure including an identity of the workpiece and one or more fields for storing information relating to the workpiece.

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

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

4. The system of claim 1, wherein the neural network is updated in substantially real time.

5. The system of claim 1, wherein the neural network is updated every 20 milliseconds.

6. The system 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.

7. The system of claim 1, wherein the inputs to the neural network include at least one process time for a workpiece within the semiconductor manufacturing system.

8. The system of claim 7, wherein the at least one process time includes one or more of a target duration, a start time, an end time, and an estimated end time.

9. The system of claim 1, wherein the inputs include a transition time.

10. The system of claim 9, wherein the transition time includes one or more of a pump down to vacuum time and a vent to atmosphere time.

11. The system of claim 1, wherein the neural network receives as inputs data from the workpiece data structure.

12. A method comprising:

controlling operation of a semiconductor manufacturing system with a neural network to schedule coordinated processing of one or more workpieces;

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 information relating to the one workpiece; and

receiving data from the semiconductor manufacturing system as an input to the neural network, where the data from the semiconductor manufacturing system embodies a position identification of an isolation valve within the system through which workpieces pass and that selectively isolates an environment of process tool from a shared vacuum environment of the system that transfers the workpieces to the process tool.

13. The method of claim 12, wherein the data from the semiconductor manufacturing system represents a state of an item of hardware within the semiconductor manufacturing system.

14. The method of claim 12, wherein the data from the semiconductor manufacturing system represents a position of a workpiece within the semiconductor manufacturing system.

15. The method of claim 12, wherein data from the semiconductor manufacturing system includes data from the workpiece data structure.

16. The method of claim 12, wherein the neural network receives as inputs data from the workpiece data structure.

17. A system comprising:

a neural network enabled controller for controlling the operation of a semiconductor manufacturing system to schedule processing of one or more workpieces, wherein the neural network receives as inputs data from the semiconductor manufacturing system, where the inputs include a transition time and the transition time includes one or more of a pump down to vacuum time and a vent to atmosphere time; and

a workpiece data structure, the data structure including an identity of the workpiece and one or more fields for storing information relating to the workpiece.

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

19. The system of claim 17, wherein the data from the semiconductor manufacturing system represents a position of a workpiece within the semiconductor manufacturing system.

20. The system of claim 17, 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.