Applications of neural networks

• US 8,442,667 B2
• Filed: 02/18/2008
• Issued: 05/14/2013
• 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.
• Primary Examiner(s): Decady, Albert
• Assistant Examiner(s): Chang, Sunray
• Application Number:
  US12/032,733
• Publication Number:
  US 20080147580A1
• Time in Patent Office: 1,912 Days
• Field of Search: None
• US Class Current: 700/121
• 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:

connecting a plurality of nodes into a neural network, each one of the nodes represented by a programming object;

defining a condition for cloning one of the plurality of nodes into a second plurality of child nodes where the one of the plurality of nodes is configured to autonomously clone and remove child nodes and the condition includes a recognition that the one of the plurality of nodes is of insufficient complexity to process inputs; and

when the condition is met, autonomously cloning the one of the plurality of nodes into two or more child nodes.

2. The method of claim 1 wherein the condition includes a processing constraint.

3. The method of claim 1 further comprising scheduling a wafer handling operation with the neural network.

4. The method of claim 3 wherein the neural network evaluates weights for a state machine.

5. A non-transitory computer-readable storage medium storing computer executable code that, when executing on one or more computing devices, performs the steps of:

connecting a plurality of nodes into a neural network, each one of the nodes represented by a programming object;

defining a condition for cloning one of the plurality of nodes into a second plurality of child nodes where the one of the plurality of nodes is configured to autonomously clone and remove child nodes and the condition includes a recognition that the one of the plurality of nodes is of insufficient complexity to process inputs; and

when the condition is met, autonomously cloning the one of the plurality of nodes into two or more child nodes.

6. The non-transitory computer program product of claim 5 wherein the condition includes a processing constraint.

7. The non-transitory computer-readable storage medium of claim 5 wherein the code further performs the step of scheduling a wafer handling operation with the neural network.

8. The non-transitory computer-readable storage medium of claim 7 wherein the neural network evaluates weights for a state machine.