Reflective Photo-mask Defect Analysis via Optical Simulation
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Solution Overview
Problem
The challenge lies in effectively inspecting and qualifying reflective photo-masks for defects, particularly in extreme ultra-violet photolithography, where defects in the multilayer stack are difficult to detect and predict, leading to unnecessary rejection and increased costs due to conservative inspection criteria.
Innovation Solution
A computer system calculates reflected light from a multilayer stack in a reflective photo-mask by determining contributions from discrete cells based on angles of incidence, polarization, and intensity, adjusting phase values, and combining these to account for defects, allowing for improved inspection and qualification criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conservative inspection criteria are used to detect defects in the multilayer stack, then defect detection reliability is improved, but photo-mask yield deteriorates due to unnecessary rejection
Solution Approach 1:
The patent replaces conservative mechanical inspection criteria with a computational optical simulation system that models light reflection from the multilayer stack. This simulation-based approach accurately predicts defect impact on photolithographic performance, enabling acceptance of masks that would be rejected by traditional conservative criteria while maintaining pattern fidelity.
Solution Approach 2:
The patent changes the inspection parameters from binary pass/fail criteria to a continuous assessment based on simulated optical performance. By evaluating actual light reflection characteristics and defect impact on aerial images, the system transforms inspection from a conservative threshold-based approach to a performance-based approach that maintains yield while ensuring quality.
2Manufacturing precision
If conservative inspection criteria are used to ensure quality, then pattern fidelity is improved, but manufacturing cost deteriorates due to increased rejection rates
Solution Approach 1:
The patent substitutes conservative inspection practices with computational optical simulation that accurately predicts pattern fidelity. By simulating light reflection and aerial image formation, the system determines whether defects actually impact pattern quality, avoiding unnecessary rejections and reducing manufacturing costs while maintaining fidelity standards.
Solution Approach 2:
The patent creates a virtual copy of the photolithographic process through optical simulation. By modeling light reflection from the multilayer stack with defects and predicting aerial image outcomes, the system evaluates pattern fidelity without physical experimentation, reducing costly trial-and-error manufacturing while ensuring quality.
3Productivity
If fast inspection methods are used to increase throughput, then productivity is improved, but measurement precision deteriorates due to inability to detect subtle defects
Solution Approach 1:
The patent replaces slow, precise physical inspection methods with fast computational optical simulation. The simulation rapidly calculates light reflection and aerial image formation to assess defect impact, providing both high throughput and precise evaluation of subtle defects that would be missed by faster but less accurate methods.
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 approach enables fast and accurate assessment of defect impact on the photolithographic process, improving reflective photo-mask yield and reducing costs by allowing more precise inspection and qualification.
Implementation Method 1
calculating reflected light from a multilayer stack in a reflective photo-mask
Implementation Method 2
adjusts phase values of the contributions to the reflected light from the multiple discrete cells, thereby specifying optical path differences between the multiple discrete cells
Implementation Method 3
selectively shifts k-space representations of the contributions to the reflected light from the multiple discrete cells based on the angles of incidence, thereby accounting for phase variations associated with deviations from normal incidence
Data Source
AI summary
During a calculation technique, contributions to reflected light from multiple discrete cells in a model of a multilayer stack in a reflective photo-mask may be determined based on angles of incidence of light in a light pattern to the multilayer stack, a polarization of the light in the light pattern, and a varying intensity of the light in the light pattern through the multilayer stack. Then, phase values of the contributions to the reflected light from the multiple discrete cells are adjusted, thereby specifying optical path differences between the multiple discrete cells in the multilayer stack that are associated with the defect. Moreover, the contributions to the reflected light from multiple discrete cells are combined to determine the reflected light from the multilayer stack. Next, k-space representations of the contributions to the reflected light from the multiple discrete cells are selectively shifted based on the angles of incidence.