Integrating CAM and process simulation to enhance on-line analysis and control of IC fabrication

1990 ◽  
Vol 3 (2) ◽  
pp. 72-79 ◽  
Author(s):  
A.J. MacDonald ◽  
A.J. Walton ◽  
J.M. Robertson ◽  
R.J. Holwill
1993 ◽  
Vol 47 (8) ◽  
pp. 1115-1122 ◽  
Author(s):  
John J. Freeman ◽  
David O. Fisher ◽  
Gregory J. Gervasio

Fourier transform (FT)-Raman spectroscopy has been applied to the online analysis and control of a PCI, reactor. This particular analytical technique was selected from a consideration of the Raman scattering efficiencies of the constituents of the reaction and the ability of the fiberoptic-coupled, near-IR, FT-Raman systems to remotely sample the toxic and potentially hazardous reaction mixture. In this communication we describe the Raman spectra of P4, PCl3, PCl5, and P4 dissolved in PCl3, as well as related compounds, along with relative band intensities of the constituents of the reaction. Factors leading to the optimum FT-Raman configuration for this particular process control problem are discussed in detail.


1994 ◽  
Vol 33 (01) ◽  
pp. 60-63 ◽  
Author(s):  
E. J. Manders ◽  
D. P. Lindstrom ◽  
B. M. Dawant

Abstract:On-line intelligent monitoring, diagnosis, and control of dynamic systems such as patients in intensive care units necessitates the context-dependent acquisition, processing, analysis, and interpretation of large amounts of possibly noisy and incomplete data. The dynamic nature of the process also requires a continuous evaluation and adaptation of the monitoring strategy to respond to changes both in the monitored patient and in the monitoring equipment. Moreover, real-time constraints may imply data losses, the importance of which has to be minimized. This paper presents a computer architecture designed to accomplish these tasks. Its main components are a model and a data abstraction module. The model provides the system with a monitoring context related to the patient status. The data abstraction module relies on that information to adapt the monitoring strategy and provide the model with the necessary information. This paper focuses on the data abstraction module and its interaction with the model.


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