Virtual Metrology Modeling for Plasma Etch Operations

• Published in: IEEE transactions on semiconductor manufacturing Vol. 22; no. 4; pp. 419 - 431
• Main Authors: Dekong Zeng, Spanos, C.J.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: New York, NY IEEE 01.11.2009; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accommodation; Applied sciences; Chambers; Electric, optical and optoelectronic circuits; Electronics; Etching; Exact sciences and technology; Input variables; Integrity; Mathematical models; Metrology; Microelectronic fabrication (materials and surfaces technology); Neural network; Neural networks; Plasma applications; Plasma chemistry; Plasma etching; Plasma properties; Predictive models; Radio frequency; Semiconductor device modeling; Semiconductor electronics. Microelectronics. Optoelectronics. Solid state devices; Semiconductors; similarity factors; variable selection; Virtual manufacturing; virtual metrology; Plasma assisted processing; Neural network; Modeling; Non linear phenomenon; virtual metrology; Microelectronic fabrication; Gas flow; Plasma etching; Reaction time; similarity factors; Accommodation; Measurement method; variable selection; Dynamic model; Data structure; Wafer
• ISSN: 0894-6507; 1558-2345
• DOI: 10.1109/TSM.2009.2031750

The objective of this paper is to present the utilization of information produced during plasma etching for the prediction of etch bias. A plasma etching process typically relies on the concentration of electrically activated chemical species in a reaction chambers over time, depending on chamber pressure, gas flow rate, power level, and other chamber and wafer properties. Plasma properties, as well as equipment factors, are complex and vary over time. In this paper, we will use various statistical techniques to address challenges due to the nature of plasma data: high dimensionality, colinearity, parameter interactions and nonlinearities, variation of data structure due to equipment condition changing over time, etc. The emphasis will be data integrity, variable selection, accommodation for process dynamics, and model-building methods. Different techniques will be evaluated with an industrial dataset.