Radiation Spectrum Optimization for Precise Lithographic Patterning
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Solution Overview
Problem
Current lithographic processes face challenges in reproducing patterns with dimensions smaller than the classical resolution limit, particularly due to the difficulty in achieving precise control over the radiation source spectrum, which affects critical dimensions and process latitude.
Innovation Solution
A method involving a computer system that determines an optimal radiation source spectrum based on design variables and lithographic metrics, utilizing machine learning models to select and combine spectral components, thereby optimizing the lithographic process for improved pattern reproduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional lithographic processes are used, then manufacturing of integrated circuits can be performed, but patterns with dimensions smaller than the classical resolution limit cannot be reproduced with sufficient precision
Solution Approach 1:
The patent applies parameter changes by optimizing the radiation source spectrum parameters (wavelength distribution, spectral shape) to achieve better pattern reproduction precision. By adjusting spectral parameters, the system overcomes the classical resolution limit while maintaining process reliability through computer-aided optimization.
2Reliability
If the radiation source spectrum is not precisely controlled, then the lithographic process can proceed, but critical dimensions and process latitude are adversely affected
Solution Approach 1:
The patent implements feedback through computer-aided optimization that uses lithographic metrics to evaluate and adjust the radiation source spectrum. The system continuously optimizes spectral parameters based on their impact on critical dimensions and process latitude, creating a closed-loop control system that maintains both reliability and precision.
3Manufacturing precision
If the radiation source spectrum is optimized using traditional methods, then some pattern reproduction can be achieved, but precise control over critical dimensions and process latitude cannot be obtained
Solution Approach 1:
The patent applies universality by creating a multi-functional computer-aided optimization system that simultaneously controls multiple spectrum parameters (wavelength, spectral shape, intensity distribution) to achieve precise critical dimension control and maximize process latitude. This unified system manages the complexity by integrating multiple functions into a single optimization platform.
Data Source
AI summary
A method that including performance of source mask optimization (SMO) for a plurality of laser spectra. The method further includes generation of training data by determining, for each optimized source-mask-combination, performance metrics such as EPE, CDU, LER, LWR, DOF, NILS for the plurality of laser spectra and training of a machine learning (ML) model to determine an optimal laser spectrum based on the generated training data. The method may further including predicting optimal spectra for production design layouts using the trained ML model.