CMP End Point Detection Using Wavelet Friction Analysis

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Solution Overview

Problem

Current end point detection methods for chemical mechanical planarization (CMP) processes are inefficient, costly, and lack robustness, particularly in detecting transitions between materials and accurately determining the end of polishing due to noise, high costs, and inefficiencies in existing sensor systems.

Innovation Solution

An online methodology using wavelet-based multiresolution analysis to decompose coefficient of friction data, followed by a sequential probability ratio test for variance, to identify significant events such as the end point in the CMP process, enabling real-time detection and reducing noise interference.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If optical sensor-based methods are used for end point detection, then thickness measurements can be obtained, but the method becomes inefficient especially with metal CMP and presents additional challenges with diffraction

Engineering Contradiction:

Improvethickness measurement accuracyVSAvoidpolishing efficiency

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent replaces optical sensor-based methods with acoustic emission sensing and wavelet-based signal processing. Acoustic emission sensors detect sound waves generated during material removal, eliminating the inefficiencies of optical methods with metals and diffraction challenges. The mechanical/acoustic field substitutes the optical field for end point detection, maintaining measurement capability while improving productivity.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Loss of time

If in-situ sensor methods are used for end point detection, then real-time detection is possible, but inaccurate interpretation of sensor data and lack of robustness occur

Engineering Contradiction:

Improvereal-time detection capabilityVSAvoiddetection robustness

Core Design Contradiction:

Loss of timeVSReliability

Solution Approach 1:

The patent implements feedback through wavelet-based multiresolution analysis of acoustic emission signals. The system continuously processes sensor data through multiple decomposition levels, comparing actual signals against expected patterns to dynamically adjust end point detection decisions. This feedback mechanism enhances robustness by adapting to varying process conditions while maintaining real-time detection capability.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent applies parameter changes by transforming acoustic emission signals through wavelet decomposition at multiple scales. By analyzing signals at different frequency resolutions and time scales, the system extracts robust features that remain reliable under varying process conditions. The multiresolution analysis parameters enable reliable detection despite changes in polishing dynamics.

Inventive Principle:

Principle #35Parameter changes

3Productivity

If coefficient of friction data is analyzed directly, then end point detection can be performed, but noise interference reduces accuracy

Engineering Contradiction:

Improvedetection speedVSAvoidend point detection accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent segments the coefficient of friction signal through wavelet decomposition into multiple frequency bands and time scales. By dividing the continuous signal into discrete wavelet coefficients at different decomposition levels, the system isolates meaningful end point indicators from noise. This segmentation enables accurate detection while maintaining processing speed through selective analysis of relevant signal components.

Inventive Principle:

Principle #1Segmentation

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

This approach provides a robust, cost-effective, and efficient method for detecting end points in CMP processes, capable of real-time implementation and accurately identifying transitions between materials, improving process control and reducing defects like over and underpolishing.

Implementation Method 1

Acoustic emission (AE) and coefficient of friction (CoF) sensors are known in the art to be used in process monitoring for EPD by measuring various properties including the amplitude of the emitted signal, and the frequency of the spectral peaks.

Methodology Applied

Scientific EffectAcoustic emission: Acoustic Emission

Implementation Method 2

Methodology Applied

Scientific EffectWavelet transform:

Implementation Method 3

Methodology Applied

Scientific EffectFriction: Friction

Data Source

PatentUS7406396B2System and method for online end point detection for use in chemical mechanical planarization

Publication Date: 2008.07.29 UNIV OF SOUTH FLORIDA

AI summary

The present invention is an online methodology for end point detection for use in a chemical mechanical planarization process which is both robust and inexpensive while overcoming some of the drawbacks of the existing end point detection approaches currently known in the art. The present invention provides a system and method for identifying a significant event in a chemical mechanical planarization process including the steps of decomposing coefficient of friction data acquired from a chemical mechanical planarization process using wavelet-based multiresolution analysis, and applying a sequential probability ratio test for variance on the decomposed data to identify a significant event in the chemical mechanical planarization process.