Light Source Calibration for Lithographic SMO Distortion
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Solution Overview
Problem
In lithographic processes for integrated circuits with smaller nodes, the Source Mask Optimization (SMO) technology faces challenges due to partial coupling between pixels in the micro-mirror array, leading to distortion in the light source pattern and deteriorated imaging effects.
Innovation Solution
A light source calibration method and system that initializes and iteratively optimizes both the light source and mask patterns using an SMO algorithm, with a pre-established error correction model to correct distortions based on input and output data sets from a Programmable Illumination System (PIS), ensuring high-fidelity pattern generation and improved process window.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If SMO algorithm is used to iteratively optimize light source and mask patterns, then lithographic resolution and process window are improved, but light source pattern distortion occurs due to partial coupling between pixels in the micro-mirror array
Solution Approach 1:
The patent applies preliminary action by establishing a light source error correction model before the SMO optimization process. The model is trained using input-output data sets from the PIS to predict and correct distortions in advance. During iterative optimization, the corrected light source pattern is updated and fed back into the SMO algorithm, preventing distortion accumulation and ensuring pattern fidelity while maintaining resolution improvement.
2Adaptability or versatility
If PIS is used to control deflection angle of micro-mirror array to achieve optimized light source pattern, then degrees of freedom for pixel pattern are increased, but partial coupling between pixels causes physical constraints that lead to pattern distortion
Solution Approach 1:
The patent implements feedback by using the light source error correction model to predict distortions caused by pixel coupling in the PIS micro-mirror array. The correction model receives the target light source pattern as input, predicts the distorted output pattern, and generates a corrected light source pattern that compensates for the coupling effects. This corrected pattern is then fed back into the SMO optimization process, allowing the system to maintain high adaptability while compensating for device complexity and physical constraints.
Data Source
AI summary
Light source calibration methods and systems employed in source mask optimization are provided. The method includes: initializing a light source pattern and a mask pattern; using an SMO algorithm to iteratively optimize the light source pattern and the mask pattern; using a pre-established light source error correction model to correct the light source pattern after each iterative optimization, and updating the light source pattern after each iterative optimization with a corrected light source pattern in a current iteration process. The light source error correction model is established according to an input and output data set consisting of an input target light source pattern and an output actual light source pattern of a PIS. The method includes determining, according to an evaluation criterion or a condition of convergence of iteration of the SMO algorithm, whether the optimization meets a requirement.