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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전자공학회 JOURNAL OF SEMICONDUCTOR TECHNOLOGY AND SCIENCE Journal of Semiconductor Technology and Science Vol.11 No.3
- 발행연도
- 2011.9
- 수록면
- 135 - 145 (11page)
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초록· 키워드
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Accurate process characterization and optimization are the first step for a successful advanced process control (APC), and they should be followed by continuous monitoring and control in order to run manufacturing processes most efficiently. In this paper, process characterization and recipe optimization methods with multiple outputs are presented in high density plasma-chemical vapor deposition (HDP-CVD) silicon dioxide deposition process. Five controllable process variables of Top SiH<SUB>4</SUB>, Bottom SiH<SUB>4</SUB>, O<SUB>2</SUB>, Top RF Power, and Bottom RF Power, and two responses of interest, such as deposition rate and uniformity, are simultaneously considered employing both statistical response surface methodology (RSM) and neural networks (NNs) based genetic algorithm (GA). Statistically, two phases of experimental design was performed, and the established statistical models were optimized using performance index (PI). Artificial intelligently, NN process model with two outputs were established, and recipe synthesis was performed employing GA. Statistical RSM offers minimum numbers of experiment to build regression models and response surface models, but the analysis of the data need to satisfy underlying assumption and statistical data analysis capability. NN based-GA does not require any underlying assumption for data modeling; however, the selection of the input data for the model establishment is important for accurate model construction. Both statistical and artificial intelligent methods suggest competitive characterization and optimization results in HDP-CVD SiD2 deposition process, and the NN based-GA method showed 26% uniformity improvement with 36% less SiH4 gas usage yielding 20.8 Å/sec deposition rate.
목차
Abstract
I. INTRODUCTION
II. EXPERMENT
III. PROCESS MODELING AND OPTIMIZATION
IV. SUMMARY AND CONCLUSIONS
ACKNOWLEDGMENT
REFERENCES
참고문헌 (11)
참고문헌 신청
1. [학술지(정기간행물)] - S. Hong / 2005 / Neural-Network-Based Sensor Fusion of Optical Emission and Mass Spectroscopy Data for Real-Time Fault Detection in Reactive Ion Etching / IEEE Trans. Ind. Electronics 52 (4) : 1062 ~ 1072
2. [학술지(정기간행물)] - G.S. May / 1991 / Statistical Experimental Design in Plasma Etch Modeling / IEEE Tans. Semi. Manufac 4 (2) : 83 ~ 98
3. [학술지(정기간행물)] - Y.H. Huang / 1994 / Constructing a Reliable Neural Network Model for a Plasma Etching Process Using Limited Experimental Data / IEEE Trans. Semi. Manufac 7 (3) : 333 ~ 344
4. [학술지(정기간행물)] - S.S. Han / 1997 / Using Neural Network Process Models to Perform PECVD Silicon Dioxide Recipe Synthesis via Genetic Algorithms / IEEE Trans. Semi. Manufac 10 (2) : 279 ~ 287
5. [학술지(정기간행물)] - S.S. Fan / 2007 / Multiple-input dual-output adjustment scheme for semiconductor manufacturing processes using a dynamic dual-response approach / European Journal of Operational Research 180 : 868 ~ 884
2. [학술지(정기간행물)] - G.S. May / 1991 / Statistical Experimental Design in Plasma Etch Modeling / IEEE Tans. Semi. Manufac 4 (2) : 83 ~ 98
3. [학술지(정기간행물)] - Y.H. Huang / 1994 / Constructing a Reliable Neural Network Model for a Plasma Etching Process Using Limited Experimental Data / IEEE Trans. Semi. Manufac 7 (3) : 333 ~ 344
4. [학술지(정기간행물)] - S.S. Han / 1997 / Using Neural Network Process Models to Perform PECVD Silicon Dioxide Recipe Synthesis via Genetic Algorithms / IEEE Trans. Semi. Manufac 10 (2) : 279 ~ 287
5. [학술지(정기간행물)] - S.S. Fan / 2007 / Multiple-input dual-output adjustment scheme for semiconductor manufacturing processes using a dynamic dual-response approach / European Journal of Operational Research 180 : 868 ~ 884
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