Reactive ion etch loading measurement technique

• US 6,268,226 B1
• Filed: 06/30/1999
• Issued: 07/31/2001
• Est. Priority Date: 06/30/1999
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Angell, David, Passow, Michael L., Burns, Stuart M., Kocon, Waldemar W.
• Primary Examiner(s): Gulakowski, Randy
• Assistant Examiner(s): SMETANA, JIRI F
• Application Number:
  US09/345,647
• Time in Patent Office: 762 Days
• Field of Search: 438/14, 438/16, 438/706, 438/712, 438/719, 216/59, 216/60
• US Class Current: 438/16
• CPC Class Codes: H01L 21/3065 Plasma etching; Reactive-ion etching
• CPC Class Codes: H01L 22/12 for structural parameters, e.g. thickness, line width, refractive index, temperature, warp, bond strength, defects, optical inspection, electrical measurement of structural dimensions, metallurgic measurement of diffusions electrical measurement of diffusions H01L22/14
• CPC Class Codes: H01L 22/20 Sequence of activities consisting of a plurality of measurements, corrections, marking or sorting steps

Abstract

A process for estimating a critical dimension of a trench formed by etching a substrate. First, a regression model is constructed for estimating the critical dimension, in which principal component loadings and principal component scores are also calculated. Next, a substrate is etched and spectral data of the etching are collected. A new principal component score is then calculated using the spectral data and the principal component loadings. Finally, the critical dimension of the trench is estimated by applying the new principal component score to the regression model.

Claims

1. A process for estimating a critical dimension of a trench formed by etching a substrate, said process comprising the steps of:

(a) constructing a regression model for estimating said critical dimension, wherein during construction of said regression model principal component loadings and principal component scores are calculated;

(b) etching said substrate and simultaneously collecting spectral data of said etching;

(c) calculating a new principal component score from said spectral data and said principal component loadings; and

(d) estimating said critical dimension by applying said new principal component score to said regression model;

wherein said step (a) of constructing a regression model comprises;

(i) etching a first substrate to form a desired critical dimension and simultaneously collecting spectral data of said etching;

(ii) etching a second substrate to form a differing critical dimension and simultaneously collecting spectral data of said etching;

(iii) calculating principal component loadings and principal component scores by analyzing the spectral data collected in (i) and (ii) using principal component analysis; and

(iv) forming a regression model using said principal component scores such that said regression model correlates said desired critical dimension to said principal component scores.

2. The process of claim 1 wherein step (ii) occurs before step (i).

3. The process of claim 1 wherein said principal component loadings capture a predetermined percentage variation between the spectral data collected in step (i) and step (ii).

4. The process of claim 3 wherein said predetermined percentage variation is at least about 90%.

7. The process of claim 1 wherein said first substrate and said second substrate are selected from the group consisting of silicon and gallium arsenide.

9. The process of claim 1 wherein said etching is reactive ion etching.

11. The process of claim 9 wherein said reactive ion etching occurs in a reactive ion etch chamber having a window and wherein a data collection device is attached to said window.

13. The process of claim 11 wherein said spectral data collected in steps (i) and (ii) are optical emission spectra.

15. the process of claim 1 wherein said spectral data collected in steps (i) and (ii) are selected from the group consisting of residual gas analyzer spectra, laser induced fluorescence spectra, and RF harmonic spectra.

17. The process of claim 1 wherein said step of collecting spectral data of said etching in steps (i) and (ii) comprises using a data collection device.

19. the process of claim 17 wherein said data collection device is an optical emission spectrometer and said spectral data are optical emission spectra.

5. A process for estimating a critical dimension of a trench formed by etching a substrate, said process comprising the steps of:

(a) constructing a regression model for estimating said critical dimension, wherein during construction of said regression model principal component loadings and principal component scores are calculated;

(b) etching said substrate and simultaneously collecting spectral data of said etching;

(c) calculating a new principal component score from said spectral data and said principal component loadings, and calculating said new principal component score from said spectral data comprises multiplying said principal component loadings times said spectral data to calculate said new principal component score, and

(d) estimating said critical dimension by applying said new principal component score to said regression model.

6. The process of claim 5 wherein said substrate is selected from the group consisting of silicon and gallium arsenide.

8. The process of claim 5 wherein said etching is reactive ion etching.

10. The process of claim 8 wherein said reactive ion etching occurs in a reactive ion etch chamber having a window and wherein a data collection device is attached to said window.

12. The process of claim 5 wherein said spectral data collected in step (b) are optical emission spectra.

14. The process of claim 5 wherein said spectral data collected in step (b) are selected from the group consisting of residual gas analyzer spectra, laser induced fluorescence spectra, and RF harmonic spectra.

16. The process of claim 5 wherein said step of collecting spectral data of said etching in step (b) comprises using a data collection device.

18. The process of claim 16 wherein said data collection device is an optical emission spectrometer and said spectral data are optical emission spectra.