Real-time diagnosis of semiconductor manufacturing equipment using a hybrid neural network expert system

IEEE transactions on components, packaging and manufacturing technology. Part C. Manufacturing

Volumn 20, Issue 1, 1997, Pages 39-47

  Kim, Byungwhan ^a   May, Gary S ^a  

^a Hyundai Electronics Industries Co   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

EXPERT SYSTEMS; FAILURE ANALYSIS; NEURAL NETWORKS; ONLINE SYSTEMS; REACTIVE ION ETCHING; REAL TIME SYSTEMS; SEMICONDUCTOR DEVICE MANUFACTURE;

CUSUM CONTROL CHART; FABRICATION EQUIPMENT;

INTEGRATED CIRCUIT MANUFACTURE;

EID: 0030679222     PISSN: 10834400     EISSN: None     Source Type: None    
DOI: 10.1109/3476.585143     Document Type: Article

References (19)

1. C. Spanos HIPPOCRATES: A methodology for IC process diagnosis Proc. ICCAD 1986
2. G. May J. Huang C. Spanos Statistical experimental design in plasma etch modeling IEEE Trans. Semiconduct. Manufact. 4 May 1991 66 2611 79720
3. J. Pan J. Tenenbaum PIES: An engineer's `Do-it-yourself' knowledge system for interpretation of parametric test data Proc. 5th Nat. Conf. on AI 1986
4. C. Himmel G. May Advantages of plasma etch modeling using neural networks over statistical techniques IEEE Trans. Semiconduct. Manufact. 6 May 1993 66 5682 216928
5. B. Kim G. May Reactive ion etching modeling using neural networks and simulated annealing IEEE Trans. Comp., Packag., Manufact. Technol. 18 Dec. 1995
6. J. Hoskins K. Kaliyur D. M. Himmelblau Fault diagnosis in complex chemical plant using artificial neural networks AlChE 37 1 1991
7. J. Murphy B. Kagle Neural network recognition of electronic malfunctions J. Intell. Manufact. 3 1992
8. G. Shafer A Mathematical Theory of Evidence 1976 Princeton Univ. Press NJ, Princeton
9. G. May C. Spanos Automated malfunction diagnosis of semiconductor fabrication equipment: A plasma etch application IEEE Trans. Semiconduct. Manufact. 6 Feb. 1993 66 5468 210656
10. M. Mocella J. Bondur T. Turner Etch process characterization using neural network methodology: A case study Proc. SPIE Module Metrology, Control, Clustering 1594 232 242 1991
11. C. Bose H. Lord Neural network models in wafer fabrication Proc. SPIE Applications Artificial Neural Networks 1965 521 530 1993
12. E. Rietman E. Lory Use of neural networks in semiconductor manufacturing processes: An example for plasma etch modeling IEEE Trans. Semiconduct. Manufact. 6 Nov. 1993 66 6689 267644
13. Y. Huang T. Edgar D. Himmelblau I. Trachtenberg Constructing a reliable neural network model for a plasma etching process using limited experimental data IEEE Trans. Semiconduct. Manufact. 7 Aug. 1994 66 7544 311337
14. R. Lippman An introduction to computing with neural nets IEEE ASSP Mag. Apr. 1987
15. M. Baker C. Himmel G. May Time series modeling of reactive ion etching using neural networks IEEE Trans. Semiconduct. Manufact. 8 Feb. 1995 66 8075 350758
16. W. Hines D. Montgomery Probability and Statistics in Engineering and Management Science 1980 Wiley New York
17. F. Nash Estimating Device Reliability: Assessment of Credibility 1993 Kluwer MA, Boston
18. G. Box W. Hunter J. Hunter Statistics for Experimenters 1978 Wiley New York
19. B. Kim G. May Estimation of weibull distribution parameters using modified back-propagation neural networks Proc. 1995 World Congr. Neural Networks I 114 117 July 1995