Method and apparatus for monitoring a process by employing principal component analysis

• US 20030136511A1
• Filed: 01/14/2003
• Published: 07/24/2003
• Est. Priority Date: 07/07/1999
• Status: Active Grant

• Current Assignee: Dimitris Lymberopoulos, Jed Davidow, Lalitha Balasubramhanya, Moshe Sarfaty
• Original Assignee: Dimitris Lymberopoulos, Jed Davidow, Lalitha Balasubramhanya, Moshe Sarfaty
• Inventors: Lymberopoulos, Dimitris, Balasubramhanya, Lalitha, Davidow, Jed, Sarfaty, Moshe
• Granted Patent: US 6,896,763 B2
• Time in Patent Office: Days
• US Class Current: 156/345.25
• CPC Class Codes: G01N 21/274 Calibration, base line adjustment, drift correction
• CPC Class Codes: G01N 21/3563 for analysing solids; Preparation of samples therefor
• CPC Class Codes: G01N 21/359 using near infrared light
• CPC Class Codes: G01N 2201/1293 resolving multicomponent spectra
• CPC Class Codes: G05B 23/024 Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks

Abstract

A method and apparatus for monitoring a process by employing principal component analysis are provided. Correlated attributes are measured for the process to be monitored (the production process). Principal component analysis then is performed on the measured correlated attributes so as to generate at least one production principal component; and the at least one production principal component is compared to a principal component associated with a calibration process (a calibration principal component). The calibration principal component is obtained by measuring correlated attributes of a calibration process, and by performing principal component analysis on the measured correlated attributes so as to generate at least one principal component. A principal component having a feature indicative of at least one of a desired process state, process event and chamber state then is identified and is designated as the calibration principal component.

Claims

1. An apparatus adapted to monitor a production process comprising:

a measurement apparatus adapted to collect optical emission spectroscopy (OES) data for electromagnetic radiation emitted by a plasma; and

a processing mechanism coupled to the measurement apparatus, the processing mechanism adapted to;

receive OES data for electromagnetic radiation emitted by a plasma during a production process performed on a production workpiece;

for a series of windows of the received OES data;

perform principal component analysis to compute a respective principal component for each window of the received OES data; and

calculate an inner product of the principal component computed for each window of the received OES data and an endpoint principal component computed for a window of OES data that corresponds to an endpoint of a previously performed calibration process; and

detect an endpoint of the production process based on the inner product calculated for each window of the received OES data.

2. The apparatus of claim 1 wherein the measurement apparatus comprises a detector adapted to detect a plurality of electromagnetic emissions from a plasma.

3. The apparatus of claim 2 wherein the detector comprises a detector selected from the group consisting of a CCD array, a photodiode array, a spectrometer and a photomultiplier.

4. The apparatus of claim 2 wherein the detector is adapted to detect electromagnetic emissions having wavelengths from about 180 to 850 nanometers.

5. The apparatus of claim 2 further comprising an optical fiber coupled to the detector, the optical fiber adapted to collect electromagnetic emissions from the plasma and to transmit the electromagnetic emissions to the detector.

6. The apparatus of claim 1 wherein the processing mechanism is further adapted to detect a transition in the calculated inner products.

7. The apparatus of claim 1 wherein the production workpiece is a silicon wafer having a multilayer semiconductor structure.

8. The apparatus of claim 1 wherein the calibration process and the production process each include etching a layer of a multilayer semiconductor structure.

9. The apparatus of claim 8 wherein the etched layer includes silicon dioxide.

10. The apparatus of claim 8 wherein the etched layer includes a metal.

11. The apparatus of claim 8 wherein the etched layer includes polysilicon.

12. The apparatus of claim 8 wherein the etched layer includes a bottom-anti-reflective-coating.

13. The apparatus of claim 1 wherein the measurement apparatus is adapted to collect electromagnetic emissions having wavelengths from about 180 to 850 nanometers.

14. The apparatus of claim 1 wherein the processing mechanism is further adapted to mean-center the received OES data before performing principal component analysis.

15. The apparatus of claim 1 wherein the processing mechanism is adapted to detect an endpoint of the production process during the producing process.

16. An automated semiconductor device fabrication tool comprising:

at least one load lock;

a wafer handler chamber coupled to the load lock, the wafer handler chamber having a wafer handler therein;

a plurality of processing chambers coupled to the wafer handler chamber; and

an apparatus coupled to at least one of the plurality of processing chambers and adapted to monitor a production process, the apparatus comprising;

a measurement apparatus adapted to collect optical emission spectroscopy (OES) data for electromagnetic radiation emitted by a plasma; and

a processing mechanism coupled to the measurement apparatus, the processing mechanism adapted to;

receive OES data for electromagnetic radiation emitted by a plasma during a production process performed on a production workpiece;

for a series of windows of the received OES data;

perform principal component analysis to compute a respective principal component for each window of the received OES data; and

calculate an inner product of the principal component computed for each window of the received OES data and an endpoint principal component computed for a window of OES data that corresponds to an endpoint of a previously performed calibration process; and

detect an endpoint of the production process based on the inner product calculated for each window of the received OES data.