Wafer Endpoint Imaging with Sampling and Illumination Tuning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing semiconductor processes face challenges in accurately determining the endpoint for material addition or removal due to variations in wafer patterns, leading to inefficient processing times and material waste.
Innovation Solution
Optimize camera sampling rates and light source intensity by analyzing wafer patterns through image-based methods and frequency domain analysis to determine optimal settings without destructive iterations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional destructive iteration methods are used to optimize endpoint determination, then accurate endpoint detection can be achieved, but processing time increases and material waste occurs
Solution Approach 1:
The system performs preliminary characterization of the wafer pattern using imaging techniques (such as metrology tools or optical microscopes) before the actual etch process. This advance analysis allows the endpoint detection parameters to be pre-configured, eliminating the need for time-consuming destructive iterations during production and achieving both accurate endpoint detection and reduced processing time.
Solution Approach 2:
Instead of using the actual wafer for iterative optimization, the system creates a digital model or representation of the wafer pattern through imaging. This copy is then used for simulation and parameter optimization, allowing accurate endpoint detection to be determined without consuming real wafers or extending production cycle times.
2Measurement precision
If traditional destructive iteration methods are used to optimize endpoint determination, then accurate endpoint detection can be achieved, but material waste increases
Solution Approach 1:
The system uses imaging to create a digital representation of the wafer pattern, which serves as a substitute for physical wafers during the optimization process. This allows endpoint detection parameters to be tuned accurately without consuming additional semiconductor materials, thereby eliminating material waste while maintaining detection precision.
Solution Approach 2:
The system uses the wafer's own optical or physical properties (captured through imaging) to determine the endpoint detection parameters. The wafer pattern itself provides the information needed for optimization, eliminating the need for separate test wafers or destructive iterations that would consume additional materials.
3Device complexity
If fixed camera sampling rates and light source intensity are used, then system complexity is reduced, but endpoint detection accuracy decreases due to wafer pattern variations
Solution Approach 1:
Instead of using uniform camera sampling rates and light source intensity across all wafers, the system analyzes the specific pattern characteristics of each wafer through imaging and tailors the detection parameters to match that particular pattern. This localized optimization maintains simple system operation while achieving high detection accuracy for each specific wafer design.
Solution Approach 2:
The system dynamically adjusts camera sampling rates and light source intensity based on the analyzed wafer pattern characteristics. By changing these parameters according to the specific pattern being processed, the system achieves accurate endpoint detection without requiring a complex fixed configuration, as the parameters are adapted to each wafer's needs.
4Productivity
If wafer pattern variations are not accounted for, then processing is faster and simpler, but endpoint detection accuracy decreases
Solution Approach 1:
The system performs advance imaging and analysis of the wafer pattern before processing begins. This preliminary characterization allows the endpoint detection parameters to be pre-configured specifically for that wafer pattern, enabling both high processing efficiency and accurate endpoint detection without requiring slow iterative adjustments during production.
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
Achieves repeatable and efficient endpoint determination, reducing processing time and material waste by optimizing camera sampling rates and light source intensity.
Implementation Method 1
determining, based on the image of the wafer, a characterization of intensities of light that will be reflected from the wafer during a semiconductor process
Data Source
AI summary
A camera may capture reflected light from the surface of the wafer during a semiconductor process that adds or removes material from the wafer, such as an etch process. To accurately determine an endpoint for the process, a camera sampling rate and light source intensity may be optimized in the process recipe. Optimizing the light source intensity may include characterizing light intensities that will be reflected from the waiver using an image of the wafer. Pixel intensities may be used to adjust the light source intensity to compensate for more complex wafer patterns. Optimizing the camera sampling rates may include nondestructively rotating a view of the wafer and converting the sampled intensities to the frequency domain. The camera sampling rate may be increased or decreased to remove spatial noise from the image without oversampling unnecessarily. These optimized parameters may then generate a clean, repeatable trace for endpoint determination.