A Control &amp; Diagnosis Scheme for Semiconductor Manufacturing

Spanos, Costas J.; Leang, S.; Sherry, Lance

doi:10.23919/acc.1993.4793454

Cited by 7 publications

(3 citation statements)

References 4 publications

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“…SUMMARY OF CSB MODELS FOR POLYSILICON UNIFORMITYPCR system can be used effectively to reduce the overall cost of ownership of semiconductor equipment [ 171,[26].…”

mentioning

confidence: 99%

Prediction of wafer state after plasma processing using real-time tool data

Lee

Spanos

1995

IEEE Trans. Semicond. Manufact.

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Empirical models based on real-time equipment signals are used to predict the outcome (e.g., etch rates and uniformity) of each wafer during and after plasma processing. Three regression and one neural network modeling methods were investigated. The models are verified on data collected several weeks after the initial experiment, demonstrating that the models built with real-time data survive small changes in the machine due to normal operation and maintenance. The predictive capability can be used to assess the quality of the wafers after processing, thereby ensuring that only wafers worth processing continue down the fabrication line. Future applications include real-time evaluation of wafer features and economical run-to-run control.

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“…SUMMARY OF CSB MODELS FOR POLYSILICON UNIFORMITYPCR system can be used effectively to reduce the overall cost of ownership of semiconductor equipment [ 171,[26].…”

mentioning

confidence: 99%

Prediction of wafer state after plasma processing using real-time tool data

Lee

Spanos

1995

IEEE Trans. Semicond. Manufact.

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“…Film thickness and refractive index were measured at five points per wafer to determine film uniformity. Film retention rate (RET) is defined as [7]. To account for the added sensitivity of this response to exposure and development, eight extra experimental runs were added to increase the resolution of the overall experiment for this response.…”

Section: Experimental Designmentioning

confidence: 99%

“…Numerous approaches to run-by-run and supervisory process control have been presented. For example, Sachs et al developed a control system to optimize low pressure chemical vapor deposition (LPCVD) of polysilicon [6], and Leang et al implemented a supervisory control system for photolithography [7].…”

Section: Introductionmentioning

confidence: 99%

Intelligent control of via formation process in MCM-L/D substrates using neural networks

Kim

May²

Proceedings International Symposium on Advanced Packaging Materials. Processes, Properties and Interfaces (IEEE Cat. No.99TH840

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Via formation is a critical process sequence in multichip module (MCM) manufacturing, as it greatly impacts yield, density, and reliability. To achieve low-cost manufacturing, modeling, optimization, and control of via formation are therefore crucial. In this paper, a model-based supervisory control algorithm is developed and applied to reduce undesirable behavior resulting from various process disturbances. Sequential neural network process models are used for system identification, and genetic algorithms are applied to update process recipes. Computer simulation results show excellent control of output response shift and drift and reduction process of variation. The supervisory control algorithm is then verified experimentally, and control results again show significant improvement in Film thickness and via yield. It is expected that this system can also improve the other process responses when suitably tuned and improved.

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Optimization of via formation in photosensitive dielectric layers using neural networks and genetic algorithms

Kim

May

1999

IEEE Trans. Electron. Packag. Manufact.

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A Control & Diagnosis Scheme for Semiconductor Manufacturing

Cited by 7 publications

References 4 publications

Prediction of wafer state after plasma processing using real-time tool data

Prediction of wafer state after plasma processing using real-time tool data

Intelligent control of via formation process in MCM-L/D substrates using neural networks

Optimization of via formation in photosensitive dielectric layers using neural networks and genetic algorithms

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