IP Library Granted Patent US 8,972,029
Granted Patent B2
US 8,972,029 · App. 11/877,203 · Granted Mar 3, 2015

Methods and systems for controlling a semiconductor fabrication process

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Quick Facts
Patent No.
US 8,972,029
App. No.
11/877,203
Granted
Mar 3, 2015
Kind
B2
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 (24)

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.

Assignments (9)
RELEASE OF SECURITY INTEREST RECORDED AT REEL/FRAME 044142/0258 Recorded Nov 4, 2025
From: MORGAN STANLEY SENIOR FUNDING, INC.
To: AZENTA, INC. (F/K/A BROOKS AUTOMATION, INC.); AZENTA US, INC. (F/K/A BROOKS LIFE SCIENCES, INC., F/K/A BIOSTORAGE TECHNOLOGIES, INC.)
Reel/Frame 073514/0609 →
RELEASE OF SECURITY INTEREST RECORDED AT REEL/FRAME 038891/0765 Recorded Nov 4, 2025
From: WELLS FARGO BANK, NATIONAL ASSOCIATION
To: AZENTA, INC. (F/K/A BROOKS AUTOMATION, INC.); AZENTA USA, INC. (F/K/A BIOSTORAGE TECHNOLOGIES, INC.)
Reel/Frame 073446/0080 →
SECOND LIEN PATENT SECURITY AGREEMENT Recorded Feb 2, 2022
From: BROOKS AUTOMATION US, LLC
To: GOLDMAN SACHS BANK USA
Reel/Frame 058945/0748 →
FIRST LIEN PATENT SECURITY AGREEMENT Recorded Feb 2, 2022
From: BROOKS AUTOMATION US, LLC
To: BARCLAYS BANK PLC
Reel/Frame 058950/0146 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 9, 2021
From: BROOKS AUTOMATION,INC
To: BROOKS AUTOMATION HOLDING, LLC
Reel/Frame 058481/0740 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 9, 2021
From: BROOKS AUTOMATION HOLDING, LLC
To: BROOKS AUTOMATION US, LLC
Reel/Frame 058482/0001 →
SECURITY INTEREST Recorded Oct 6, 2017
From: BROOKS AUTOMATION, INC.; BIOSTORAGE TECHNOLOGIES, INC.
To: MORGAN STANLEY SENIOR FUNDING, INC.
Reel/Frame 044142/0258 →
SECURITY AGREEMENT Recorded May 31, 2016
From: BROOKS AUTOMATION, INC.; BIOSTORAGE TECHNOLOGIES
To: WELLS FARGO BANK, NATIONAL ASSOCIATION
Reel/Frame 038891/0765 →
BILL OF SALE Recorded Oct 16, 2012
From: BLUESHIFT TECHNOLOGIES, INC.
To: BROOKS AUTOMATION, INC.
Reel/Frame 029134/0283 →