Lithographic Sensor Initialization for Overlay Accuracy
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Solution Overview
Problem
Conventional lithographic apparatuses face challenges in accurately determining the position of alignment marks on substrates due to deformations and measurement parameter dependencies, leading to overlay errors during the exposure process.
Innovation Solution
An initialization method for metrology sensors that involves simulating multiple measurements using different parameters, estimating characteristics, determining biases, and optimizing a cost function to derive weighting coefficients for improved alignment and position determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional alignment sensors are used to measure alignment mark positions, then the measurement process is simple, but measurement precision deteriorates due to deformation dependencies and parameter sensitivities
Solution Approach 1:
The patent applies preliminary action by performing simulations and determining weighting coefficients before actual measurements. The sensor is initialized by simulating measurements with different parameters, calculating biases, and optimizing weighting coefficients in advance. This preparatory work enables the sensor to compensate for deformation dependencies during actual measurements without adding complexity to the measurement process itself.
Solution Approach 2:
The patent employs parameter changes by varying measurement parameters in simulations to determine optimal weighting coefficients. Different simulation parameters are used to model various measurement conditions and deformations, allowing the system to adapt to different scenarios. The optimized weighting coefficients adjust the contribution of different measurement parameters to achieve accurate position determination despite deformations.
2Measurement precision
If multiple measurements with different parameters are performed to account for deformations, then measurement precision improves, but measurement time and process complexity increase
Solution Approach 1:
The patent performs all time-consuming simulations and weighting coefficient optimizations in advance during sensor initialization. By completing these computationally intensive tasks before actual measurements, the system avoids repeating them for each measurement, thus improving measurement precision without significantly increasing measurement time.
Solution Approach 2:
The patent uses simulations as copies of actual measurements to determine weighting coefficients. Instead of performing multiple physical measurements with different parameters, the system creates virtual measurement scenarios through simulations, analyzes the results, and derives optimal weighting coefficients. This copying approach reduces the need for repeated physical measurements while maintaining measurement accuracy.
3Manufacturing precision
If weighting coefficients are optimized to account for measurement parameter dependencies, then manufacturing precision improves, but device complexity increases
Solution Approach 1:
The patent optimizes weighting coefficients by varying simulation parameters to model different measurement conditions and substrate deformations. This parameter variation allows the system to determine optimal weights for different measurement parameters, improving overlay accuracy by compensating for deformation dependencies. The optimization process, while computationally intensive, establishes a robust measurement system that handles various scenarios.
Solution Approach 2:
The patent implements feedback through the optimization of weighting coefficients based on simulation results. The simulated measurement outcomes feed back into the determination of weighting coefficients, which are then used to improve actual measurements. This feedback loop enables the system to learn from simulated scenarios and apply the learned weights to real measurements, enhancing manufacturing precision.
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 method enhances the accuracy of alignment mark position determination, reducing overlay errors by accounting for measurement parameter dependencies and substrate deformations, thereby improving the alignment process in lithographic apparatuses.
Implementation Method 1
an alignment sensor is applied which may e.g. be configured to project a radiation beam onto an alignment mark or target and determine, based on a reflected radiation beam, a position of the alignment mark
Data Source
AI summary
An initialization method including estimating a characteristic of a property of an object based on a plurality of measurements by the sensor of the property using a respective plurality of different measurement parameters, different ones of the measurements using different measurement parameters, the characteristic including a combination of respective outcomes of respective ones of the measurements weighted by a respective weighting coefficient; performing, for each of a plurality of models of the object, each model configured to enable respective simulation of the performing of the measurements, a respective simulation, the respective simulation including simulating the measurements under control of a respective plurality of different simulation parameters to obtain a respective plurality of simulated characteristics of the property, the different simulation parameters being indicative of the different measurement parameters; determining, for each of the models, a respective bias representative of a respective difference between a respective theoretical characteristic of the property in accordance with the respective model and a respective further combination of the simulated characteristics of the property in the respective model, the respective further combination of the simulated characteristics including the weight coefficients, each particular one of the weight coefficients associated with a particular one of the different simulation parameters; using a cost function configured to optimize a correspondence between the simulated characteristic of the property and the theoretical characteristic of the property, the cost function being a function of the respective biases of the models; and optimizing the cost function to derive the weight coefficients from the cost function; and using the weight coefficients and the associated simulation parameters in a controller associated with the sensor.