SRAF Defect Prediction from Photolithography Mask Aerial Images
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Solution Overview
Problem
Current methods for detecting sub-resolution assist feature (SRAF) related defects in photolithography masks require actual wafer printing, which is resource-intensive and time-consuming, and existing OPC techniques fail to account for all 2D or 3D pattern combinations, leading to defects and narrowing the process window.
Innovation Solution
A computer-implemented method generates an aerial image of the photolithography mask to predict SRAF-related defects by identifying critical locations based on predefined constraints, comparing them to a reference image, and modifying the mask model to prevent defects without actual wafer printing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If actual wafer printing is performed to detect SRAF-related defects, then defect detection accuracy is improved, but resource consumption and time required increase significantly
Solution Approach 1:
The patent creates a computational copy (aerial image simulation) of the photolithography mask pattern and processes this digital representation instead of physically printing wafers. The aerial image serves as a virtual replica that captures the essential pattern information, allowing defect prediction through software analysis rather than physical manufacturing and inspection.
Solution Approach 2:
The patent performs defect prediction analysis on the aerial image before actual wafer printing occurs. By identifying critical locations and predicting potential defects in advance using the simulated aerial image, the system can prevent defective wafers from being manufactured, thereby avoiding wasted resources and time on faulty products.
2Manufacturing precision
If existing OPC techniques are used to correct patterns, then pattern printing accuracy is improved, but they fail to account for all 2D or 3D pattern combinations leading to defects
Solution Approach 1:
The patent changes the approach from modifying mask patterns (OPC) to simulating the optical field distribution (aerial image) and analyzing it computationally. By transforming the problem into an optical physics simulation rather than a pattern correction exercise, the system can accurately predict defects across all possible 2D and 3D pattern combinations without relying on approximation rules.
Solution Approach 2:
The patent introduces the aerial image as an intermediary representation between the mask pattern and the final wafer pattern. This aerial image simulation acts as a mediator that captures the optical proximity effects and interference patterns, allowing accurate defect prediction without directly manipulating the mask design or requiring physical wafer printing.
3Measurement precision
If comprehensive defect analysis is performed on all mask locations, then detection thoroughness is improved, but computational runtime increases
Solution Approach 1:
The patent segments the mask pattern analysis into two stages: first identifying critical locations based on pattern characteristics (such as line ends, corners, and dense regions), then performing detailed aerial image simulation and defect prediction only at these critical locations. This segmentation dramatically reduces the computational domain while maintaining comprehensive defect detection capability.
Solution Approach 2:
The patent applies different levels of analysis to different regions of the mask. Rather than uniformly processing the entire mask, the system identifies locations with higher defect risk (critical locations) and concentrates computational resources there. The aerial image simulation and defect prediction are performed with high precision only at these critical locations, optimizing the balance between thoroughness and runtime.
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
Enables accurate, efficient, and resource-saving prediction of SRAF-related defects and process window qualification, improving throughput and reducing runtime by focusing on critical locations and using machine learning for enhanced accuracy.
Implementation Method 1
During the printing process an illuminated image projected from the photolithography mask is focused onto a photoresist thin film formed on the substrate
Implementation Method 2
an illuminated image projected from the photolithography mask is focused onto a photoresist thin film
Implementation Method 3
The optical proximity effect caused by non-uniformity of energy intensity due to optical diffraction during the exposure process occurs
Data Source
AI summary
A computer implemented method for inspecting a photolithography mask to predict defects in wafers, the method comprising: providing a model of the photolithography mask comprising one or more target features and one or more sub-resolution assist features, the photolithography mask being configured for printing of the one or more target features onto a wafer in a printing process using a photolithography system; identifying one or more critical locations in the model of the photolithography mask by verifying a predefined constraint concerning the target features and/or the sub-resolution assist features; generating an aerial image of the photolithography mask comprising the one or more identified critical locations by applying a model of the photolithography system to the photolithography mask; predicting defects in wafers by comparing the one or more identified critical locations of the aerial image to one or more corresponding locations of a reference image. Also disclosed are a computer-readable medium, a computer program product and corresponding systems for the prediction of defects in wafers and wafer-less process window qualification.