Lithography Alignment Weighting for Overlay Error Reduction
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Solution Overview
Problem
Lithographic methods face challenges in accurately aligning substrates due to deviations between measured and actual positions of alignment marks, often caused by substrate deformations from processes like etching or chemical mechanical polishing, leading to overlay errors.
Innovation Solution
A method involving optimal color weighting (OCW) is employed to combine alignment measurements using different operational parameters, such as wavelengths and polarizations, to separate the effects of substrate and alignment mark deformations, determining weights to minimize overlay errors by training with overlay data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If alignment measurements are taken using a single operational parameter, then the measurement process is simple, but alignment accuracy deteriorates due to substrate and alignment mark deformations
Solution Approach 1:
The measurement process is segmented into multiple independent measurements taken at different operational parameters (wavelengths, polarizations). Each measurement provides partial information about the alignment mark position, and these segmented measurements are later combined to achieve high-precision alignment that compensates for substrate and mark deformations.
Solution Approach 2:
The measurement is performed by changing operational parameters such as wavelength and polarization state. By taking measurements at multiple parameter values and combining them with appropriate weights, the system achieves high alignment accuracy while accounting for deformations that affect different parameters differently.
2Measurement precision
If multiple alignment measurements at different operational parameters are combined, then alignment accuracy improves, but the complexity of determining optimal weights increases
Solution Approach 1:
The system uses feedback from previously measured overlay errors to iteratively optimize the weights assigned to different operational parameters. The measured overlay data is fed back into the weighting optimization process, allowing the system to learn and adjust weights to minimize alignment errors, thereby improving accuracy while managing processing complexity through adaptive learning.
Solution Approach 2:
Multiple copies of the alignment measurement are taken at different operational parameters (wavelengths, polarizations). These redundant measurements serve as independent data sources that, when combined with optimized weights, provide robust alignment information that compensates for deformations affecting individual measurements.
3Productivity
If substrate processing steps like etching and CMP are performed, then device manufacturing is enabled, but alignment mark deformation occurs leading to overlay errors
Solution Approach 1:
The system performs preliminary alignment measurements at multiple operational parameters before the harmful deformation fully impacts the final alignment. By measuring and combining data from different parameters, the system can predict and compensate for deformation effects, enabling accurate alignment even after substrate processing steps like etching and CMP.
Solution Approach 2:
The system converts the harmful effect of alignment mark deformation into a solvable problem by measuring how different operational parameters are affected differently by the deformation. By analyzing the differential impact of deformations across multiple parameters and using optimized weighting, the system recovers accurate alignment information that would be impossible to obtain from a single measurement.
Data Source
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AI summary
A method for determining one or more optimized values of an operational parameter of a sensor system configured for measuring a property of a substrate is disclosed the method comprising: determining a quality parameter for a plurality of substrates; determining measurement parameters for the plurality of substrates obtained using the sensor system for a plurality of values of the operational parameter; comparing a substrate to substrate variation of the quality parameter and a substrate to substrate variation of a mapping of the measurement parameters; and determining the one or more optimized values of the operational parameter based on the comparing.