Substrate Characteristic Modeling with Adaptive OCD Calibration
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Solution Overview
Problem
Existing optical critical dimension (OCD) metrology methods are inflexible and lack versatility in calibrating substrate characteristics, leading to challenges in accurately capturing complex manufacturing processes due to insufficient or excessive degrees of freedom in model calibration.
Innovation Solution
A system and method that employs a range of calibration techniques, including temporal, frame-wise, multi-model, and hybrid approaches, with regularization, to flexibly adjust the number of fitting parameters, enabling accurate and efficient in-situ monitoring of substrate characteristics during manufacturing processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional OCD metrology methods use fixed model calibration approaches, then the calibration process is simple, but the accuracy of substrate characteristic measurement deteriorates due to insufficient flexibility in capturing complex manufacturing processes
Solution Approach 1:
The patent implements dynamic model calibration by allowing the system to switch between different calibration approaches (single-time-point, multi-time-point, and temporal calibration engines) based on process complexity. The calibration model adapts its structure and parameters dynamically during substrate processing, enabling accurate capture of time-varying substrate characteristics without requiring a fixed complex model structure.
Solution Approach 2:
The system changes calibration parameters including the number of fitting parameters, time-point selections, and model structures based on process requirements. By adjusting these parameters dynamically, the system achieves high measurement accuracy for complex processes while maintaining operational simplicity through automated parameter selection.
2Measurement precision
If the calibration model uses more fitting parameters to capture complex substrate evolution, then the measurement accuracy improves, but the computational complexity and calibration time increase
Solution Approach 1:
The calibration process is segmented into distinct calibration engines operating at different time points during substrate processing. Each calibration engine handles specific calibration tasks for particular time intervals, allowing the system to distribute computational load and reduce overall calibration time while maintaining high measurement accuracy through cumulative calibration results.
Solution Approach 2:
The system performs preliminary calibration at initial time points using simplified models, then progressively refines the calibration with additional time-point data as the process evolves. This staged approach allows the system to establish baseline measurements quickly and then improve accuracy incrementally without requiring all calibration computations to be performed simultaneously.
3Adaptability or versatility
If a single calibration approach is used for all substrate processing scenarios, then the system is easy to operate, but the versatility and adaptability to different process complexities deteriorates
Solution Approach 1:
The patent implements a universal calibration platform that incorporates multiple calibration engines (single-time-point, multi-time-point, and temporal calibration engines) within a single system. This multi-functional architecture allows the system to adapt to different process complexities and substrate types while maintaining a unified user interface and operational workflow, preserving ease of operation despite enhanced versatility.
Solution Approach 2:
The system dynamically selects and switches between different calibration approaches based on the specific substrate processing scenario and process complexity. This dynamic adaptability allows a single system to provide versatile calibration solutions for various applications without requiring manual reconfiguration, maintaining operational simplicity while achieving high adaptability.
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
Enhances the accuracy and versatility of in-situ OCD inference by allowing for customizable model selection based on process complexity, providing precise and timely insights into substrate evolution, thereby improving manufacturing efficiency and product quality.
Implementation Method 1
The emitted light interacts with the substrate, and its reflected or diffracted light is directed to a sensor where its properties are measured and recorded
Implementation Method 2
The emitted light interacts with the substrate, and its reflected or diffracted light is directed to a sensor where its properties are measured and recorded
Data Source
AI summary
A method for estimating process characteristics is provided. The method can include collecting process data from a spectral emitter and a spectral sensor during a substrate processing operation, and generating a calibrated model for the process data. Generating a calibrated model can include selecting a calibration option from a set of calibration options, based on a degree of freedom associated with a given calibration option, and calibrating a base model to generate the calibrated model. The base model is calibrated using the selected calibration option and a portion of the first process data.