Methods and systems for controlling a semiconductor fabrication process

• US 8,473,270 B2
• Filed: 10/23/2007
• Issued: 06/25/2013
• 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): Rivas, Omar Fernandez
• Assistant Examiner(s): LUU, CUONG V
• Application Number:
  US11/877,214
• Publication Number:
  US 20080155447A1
• Time in Patent Office: 2,072 Days
• Field of Search: 702/120, 700/100, 700/31, 714/25, 414/217, 454/187
• US Class Current: 703/14
• 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 method comprising:

controlling operation of a semiconductor manufacturing system with a state machine and generating, with the state machine, at least in part a schedule for processing of one or more workpieces, the state machine including a plurality of states associated with a plurality of transitions, each one of the plurality of transitions having a weight assigned thereto and each state corresponds to a workpiece processing function of a piece of equipment of the semiconductor manufacturing system, 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;

receiving data from the semiconductor manufacturing system;

calculating the weight assigned to each one of a number of possible transitions from a current state of the plurality of states for a predetermined piece of equipment of the semiconductor manufacturing system by applying the data as inputs to a neural network; and

selecting a transition of the workpiece processing function of the predetermined piece of equipment to a second state from the current state of the plurality of states by evaluating the weight assigned to each one of the number of possible transitions from the current state,

wherein the state machine sends control signals to the semiconductor manufacturing system and control processing that re-evaluates or reconfigures the state machine is separated from operation of the state machine such that the state machine operates to send the control signals independent of the neural network to provide transitions among states according to inputs from the semiconductor manufacturing system.

2. The method of claim 1, wherein the semiconductor manufacturing system includes a tunnel-based cart workpiece transport facility combined with a robot-robot handoff linear processing facility.

3. The method of claim 2, wherein the semiconductor manufacturing system comprises a vacuum environment for processing the one or more workpieces and a workpiece transport facility outside of the vacuum environment.

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

5. The method of claim 4, wherein at least one semiconductor manufacturing module can be added to the semiconductor manufacturing system without reconfiguring existing modules.

6. The method of claim 1, wherein processing of one or more workpieces includes at least one of heating one or more workpieces and cooling one or more workpieces.

7. The method of claim 1, wherein the received data includes technical information related to a process associated with the workpiece.

8. The method of claim 1, wherein the received data includes workpiece edge sensing data.

9. The method of claim 1, wherein at least one of the states represents a state of an item of hardware within the semiconductor manufacturing system.

10. The method of claim 1, wherein at least one of the states represents a position of a workpiece within the semiconductor manufacturing system.

11. The method of claim 1, wherein at least one of the states represents a position of an isolation valve within the system.

12. The method of claim 1, wherein the state machine is updated within a predetermined time interval.

13. The method of claim 1, wherein the state machine is updated every 20 milliseconds.

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

15. The method of claim 14, 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.

16. 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.

21. The method of claim 1, wherein the state machine is configured to operate without input from the neural network based on inputs from the semiconductor manufacturing system and the neural network is configured to evaluate outputs where processing time for evaluating the outputs extends over multiple time increments of the finite state machine.

17. 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 state machine and generating, with the state machine, at least in part a schedule for processing of one or more workpieces, the state machine including a plurality of states associated with a plurality of transitions, each one of the plurality of transitions having a weight assigned thereto and each state corresponds to a workpiece processing function of a piece of equipment of the semiconductor manufacturing system, 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;

receiving data from the semiconductor manufacturing system;

calculating the weight assigned to each one of a number of possible transitions from a current state of the plurality of states for a predetermined piece of equipment of the semiconductor manufacturing system by applying the data as inputs to a neural network; and

selecting a transition of the workpiece processing function of the predetermined piece of equipment to a second state from the current state of the plurality of states by evaluating the weight assigned to each one of the number of possible transitions from the current state,

wherein the state machine sends control signals to the semiconductor manufacturing system and control processing that re-evaluates or reconfigures the state machine is separated from operation of the state machine such that the state machine operates to send the control signals independent of the neural network to provide transitions among states according to input from the semiconductor manufacturing system.

18. A system comprising:

a state machine that controls operation of a semiconductor manufacturing system and generates at least in part a schedule for 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, the state machine including a plurality of states associated with a plurality of transitions, each one of the plurality of transitions having a weight assigned thereto and each state corresponds to a workpiece processing function of a piece of equipment of the semiconductor manufacturing system, wherein when the state machine is operating within one of the plurality of states, a selection of a transition of the workpiece processing function of a predetermined piece of equipment of the semiconductor manufacturing system from the one of the plurality of states to another one of the plurality of states is determined by evaluating the weight assigned to each one of a number of possible transitions from the one of the plurality of states; and

a neural network that receives as inputs data from the semiconductor manufacturing system and provides as outputs the weights for one or more of the plurality of transitions,

wherein the state machine is configured to send control signals to the semiconductor manufacturing system and control processing that re-evaluates or reconfigures the state machine is separated from operation of the state machine such that the state machine operates to send the control signals independent of the neural network to provide transitions among states according to input from the semiconductor manufacturing system.

19. The method of claim 18, wherein at least one of the states represents a state of an item of hardware within the semiconductor manufacturing system.

20. The method of claim 18, wherein at least one of the states represents a position of a workpiece within the semiconductor manufacturing system.