Diffusion Model Parameter Inference for Wafer Metrology
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Solution Overview
Problem
Current metrology data processing techniques are computationally intensive, prone to getting stuck in local minima, and fail to accurately model patterned substrates due to insufficient parameterization, especially when extra layers like oxidation are present.
Innovation Solution
Employ a generative neural network, specifically a diffusion model, to iteratively refine substrate model data, making it more consistent with metrology data, using sensor configuration data and class information to enhance robustness and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If iterative optimization algorithms are used to infer parameters from metrology data, then parameter estimation can be obtained, but the computational burden is intensive and the process is time-consuming
Solution Approach 1:
The patent pre-trains a diffusion model on a large dataset of substrate structures and their corresponding metrology data before actual parameter inference. This preliminary training enables the model to learn complex relationships offline, so that during actual use, parameter inference can be performed rapidly without iterative optimization, thus reducing computational time while maintaining accuracy
Solution Approach 2:
The patent uses a diffusion model that generates parameter estimates by learning from trained data distributions rather than performing iterative optimization from scratch. The model creates a probabilistic copy of the parameter inference process based on patterns learned during training, enabling fast approximation without repeated forward model evaluations
2Manufacturing precision
If sophisticated fine-tuning steps and resolution enhancement techniques are applied to achieve low k1 lithography, then pattern reproduction accuracy is improved, but the process complexity increases
Solution Approach 1:
The patent infers multiple substrate parameters simultaneously (including thickness, refractive index, absorption coefficient, and roughness) using a unified diffusion model. By changing from traditional single-parameter optimization to multi-parameter joint inference, the system achieves accurate pattern reproduction while simplifying the overall process through integrated parameter estimation
3Measurement precision
If traditional parameter inference methods are used, then parameter estimates can be obtained, but the methods fail to accurately model substrates with extra layers such as oxidation
Solution Approach 1:
The patent employs a diffusion model that is trained on diverse substrate structures including various layer configurations, oxidation layers, and different material compositions. This universal training enables the single model to accurately infer parameters for substrates with extra layers or unexpected structures without requiring model reconfiguration, thus achieving both high accuracy and adaptability
Data Source
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AI summary
A generative network, conditioned on metrology data obtained from at least one patterned substrate, is used to process an input, to generate an output. The input is substrate model data defining a substrate model of the at least one substrate, and the output of the generative network is used to produce updated substrate model data defining an updated substrate model. In one or more further iterations, the generative network processes an input based on the updated substrate model generated in the preceding operation. The output of the generative network in the last iteration is used to generate substrate description data describing the at least one substrate.