We analyze the problem of real-time thickness estimation for patterned wafers during an etching process using in situ spectroscopic ellipsometry. For that, a two-stage estimation algorithm is proposed. The first stage is an automatic model calibration algorithm that uses the data collected during an initial interval. The second stage is a nonlinear state estimation system designed for the tuned model. We study the sensitivity of this estimation strategy to variations in the wafer parameters and the process conditions.

https://ieeexplore.ieee.org/iel5/6503/17370/00807759.pdf

Real-time estimation of patterned wafer parameters using in situ spectroscopic ellipsometry

: The MURI Center on Modeling and Control of Plasma Processing at the University of Michigan started in September, 1995, and concluded technical work at the end of August 2001. As the name indicates, the major research goals of the center are in the areas of modeling and control of plasma deposition and etching processing. These plasma processes are used extensively in the manufacture of integrated circuits as well as active matrix liquid crystal displays. These applications areas motivate our selection of research problems in modeling and control. Significant accomplishments were made in all of these areas (as will be discussed in the body of the report) Particular program highlights include: (1) An optical technique was developed to monitor in situ and in real time the critical dimensions and wall-shapes of evolving features in reactive ion etchers. An advanced signal processing scheme was devised to use this technique to perform the first fully-automated etch-to-target-dimension etches. One-nanometer-level (or better) accuracy was demonstrated enabling possibilities for extremely high accuracy semiconductor fabrication control. (2) The state-of-the-art of 1st principles plasma equipment modeling was advanced so that the entire system of the sensors, plasma process equipment, and control systems could be modeled numerically. (3) Novel RF Sensing to non-invasively measure the electrical state of plasma systems was developed and applications to detecting common faults were demonstrated. (4) Improved statistical methods for detecting and identifying the causes of spatially clustered defects in semiconductor manufacturing. (5) Development of a novel ion-beam modification process for the deposition of Al films which are more resistant to grain-growth.

/pdf/intelligent-electronics-manufacturing-modeling-and-control-4yy4uf9c4y.pdf

Intelligent Electronics Manufacturing: Modeling and Control of Plasma Processing

We analyze the problem of estimating product variables from process measurements in manufacturing systems. In particular, a novel approach for studying the performance of such estimators in terms of their expected performance is introduced. Using ideas from statistical learning theory, we obtain sufficient conditions on the manufacturing process, the estimation algorithm, and the design procedure to guarantee asymptotic convergence of the estimation algorithm to some optimal estimator when the available data goes to infinity.

C.G. Galarza

Papers

A learning and estimation problem arising from in situ control and diagnostics of manufacturing processes