CMP Thickness Monitoring With Polishing Pad Wear Compensation
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Solution Overview
Problem
Chemical mechanical polishing (CMP) processes face challenges in accurately monitoring substrate thickness and compensating for signal distortions caused by polishing pad thickness variations, leading to inconsistencies in polishing endpoint and non-uniformity across wafers.
Innovation Solution
A neural network is trained using thickness measurements corresponding to different polishing pad thicknesses to generate corrected thickness values, which are used to compensate for signal distortions and improve endpoint control and within-wafer and wafer-to-wafer non-uniformity by adjusting polishing parameters in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of stationary object
If polishing pad thickness varies due to conditioning wear, then polishing process continues over time, but measurement precision of substrate thickness deteriorates due to signal distortions
Solution Approach 1:
The system performs preliminary calibration by measuring substrate thickness at multiple known polishing pad thicknesses before actual polishing. These preliminary measurements are used to generate a compensation model that accounts for signal distortions at different pad thicknesses, enabling accurate thickness measurement throughout the polishing process despite pad wear.
Solution Approach 2:
The system changes the parameter of pad thickness compensation by measuring at multiple discrete pad thickness points and interpolating between them. The compensation model dynamically adjusts the interpretation of thickness measurements based on the current estimated pad thickness, maintaining measurement accuracy as the pad wears during polishing.
2Loss of time
If in-situ monitoring is used to track substrate thickness during polishing, then real-time endpoint detection is improved, but measurement precision deteriorates due to signal distortions from varying pad thickness
Solution Approach 1:
The system implements feedback by continuously monitoring substrate thickness during polishing and comparing it against the compensation model. The model is updated based on measured thickness values and known pad wear, allowing real-time correction of signal distortions and accurate endpoint detection throughout the polishing process.
Solution Approach 2:
The compensation model is established through preliminary calibration measurements taken at multiple pad thicknesses before polishing begins. This pre-established model enables real-time correction during polishing without requiring additional calibration steps, maintaining both speed and accuracy.
3Reliability
If polishing pad is conditioned to maintain abrasive uniformity, then polishing consistency is improved, but pad thickness varies due to wear, causing signal distortions
Solution Approach 1:
The system accounts for parameter changes in pad thickness by measuring substrate thickness at multiple known pad thickness values during calibration. These measurements are used to create a compensation model that adjusts thickness interpretation based on current pad thickness, maintaining measurement precision despite the necessary pad wear from conditioning.
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
The solution effectively compensates for signal distortions near the substrate edge and pad thickness variations, enhancing polishing uniformity and consistency across wafers by using a neural network to process thickness measurements and adjust polishing parameters accordingly.
Implementation Method 1
an eddy current sensing system may be used to induce eddy currents in a conductive region on the substrate to determine parameters such as the local thickness of the conductive region
Data Source
AI summary
A method of training a neural network includes obtaining two ground truth thickness profiles a test substrate, obtaining two thickness profiles for the test substrate as measured by an in-situ monitoring system while the test substrate is on polishing pads of different thicknesses, generating an estimated thickness profile for another thickness value that is between the two thickness values by interpolating between the two profiles, and training a neural network using the estimated thickness profile.