Production pattern-recognition artificial neural net (ANN) with event-response expert system (ES)--yieldshieldTM

• US 20030028353A1
• Filed: 08/06/2001
• Published: 02/06/2003
• Est. Priority Date: 08/06/2001
• Status: Abandoned Application

• Current Assignee: Nokia Mobile Phones UK Limited (Nokia Corporation)
• Original Assignee: Nokia Mobile Phones UK Limited (Nokia Corporation)
• Inventors: Gventer, Brian
• Application Number:
  US09/923,215
• Publication Number:
  US 20030028353A1
• Time in Patent Office: Days
• US Class Current: 702/182
• CPC Class Codes: G05B 13/027 using neural networks only
• CPC Class Codes: G05B 19/41875 characterised by quality surveillance of production
• CPC Class Codes: G05B 2219/31354 Hybrid expert, knowledge based system combined with ann
• CPC Class Codes: G05B 2219/32193 Ann, neural base quality management
• CPC Class Codes: G05B 23/024 Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
• CPC Class Codes: G05B 23/027 Alarm generation, e.g. communication protocol; Forms of alarm
• CPC Class Codes: G05B 23/0281 Quantitative, e.g. mathematical distance; Clustering; Neural networks; Statistical analysis
• CPC Class Codes: G06N 3/042 Knowledge-based neural networks; Logical representations of neural networks
• CPC Class Codes: Y02P 90/02 Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Abstract

Artificial Neural Net (ANN) coupled with an Expert System (ES) which monitors production test plans in real-time is provided. The ANN recognizes and classifies production yield patterns occurring at individual tester, complete test stage, and production line test aggregation and executes a proscribed range of responses. The ANN will automate human statistical analysis and line monitoring functions, identify emerging yield trends, identify proximate cause of a yield-degrading event, classify event severity, and provide conclusional accuracy. The ES, based on recognized or inferred conditions provided by the ANN, consults it'"'"'s knowledge base and applies cognitive heuristics to execute responses in the manner described by the human expert it is modeled after. These responses may include a summary report electronically to the correct individuals, a voice/pager message to the individuals responsible to react to an event, a visual or audible alarm at the event site, and/or direct adjustment of the production process

Claims

1. A system for monitoring a manufacturing production line, said system comprising:

an artificial neural network (ANN) for recognizing and classifying production yield patterns; and

an expert system (ES) coupled to said artificial neural network to provide a knowledge base and apply cognitive heuristics to execute responses based on production yield patterns information received from said artificial neural network.

2. The system of claim 1 wherein said ANN identifies a plurality of yield trends and assigns a weight to at least one of said production yield trends;

and wherein said ANN outputs notification for identifying each of said at least one weighted production yield trend having an assigned weight beyond a predetermined yield-degrading threshold value.

3. The system of claim 1, further comprising instructions for training said ANN to assign said weight to each of said production yield trends based on historical case studies.

4. The system of claim 1, further comprising instructions for training said ANN to assign said weight to each of said production yield trends based on a base of knowledge.

5. The system of claim 1, further comprising instructions to send a report to predetermined individuals.

6. The system of claim 1, further comprising instructions to provide an alarm signal.

7. The system of claim 1, further comprising instructions to send a pager message to predetermined individuals.

8. The system of claim 1, further comprising instructions to adjust the production process in accordance with the knowledge base of the system.

9. A method for system for monitoring a manufacturing production line using an artificial neural network (ANN) coupled to an expert system, said method comprising the steps of:

recognizing a plurality of production yield patterns;

classifying at least one of said production yield patterns into at least one production yield trend;

weighting said at least one production yield trend;

providing notification to expert system (ES) when at least one of said weighted trends passes a predetermined yield-degrading threshold value; and

executing responses from said expert system (ES) in accordance with said expert systems knowledge base.