Sample Map Modeling for Nonlinear Wafer Field Alignment
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Solution Overview
Problem
Current sample assessment methods for identifying defects in semiconductor IC chip manufacturing are inefficient due to inaccuracies in pattern inspection, leading to reduced throughput and increased operator intervention, as they struggle to accurately locate features on substrates with non-linear deviations in field region positions and shapes.
Innovation Solution
A method involving the measurement of first and second marks on field regions, fitting models to represent their positions and shapes, and generating a sample map using these models, which can account for non-linear geometries, combined with exposure correction data to improve alignment and inspection efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional alignment mark measurement methods are used, then the process is simple, but the accuracy of field region positioning is insufficient due to non-linear deviations
Solution Approach 1:
The patent applies preliminary action by measuring alignment marks and fitting models to represent field region positions and shapes before the actual inspection process. This pre-characterization of the sample map allows the inspection system to accurately locate features of interest without dealing with non-linear deviations during inspection, thereby improving measurement precision while managing complexity through advance preparation
Solution Approach 2:
The patent introduces an intermediary model fitting approach where mathematical models serve as mediators between the physical alignment marks and the digital sample map. These models translate measured mark positions into accurate field region representations, enabling precise positioning without directly complex handling of non-linear geometric deviations
2Reliability
If accurate sample map generation is implemented, then defect identification reliability improves, but processing time increases
Solution Approach 1:
The patent uses preliminary action by generating accurate sample maps in advance before inspection. By pre-fitting models to alignment mark data and creating the sample map beforehand, the system ensures high defect identification reliability while allowing time-consuming operations to be performed outside the critical inspection path
Solution Approach 2:
The patent replaces manual or iterative sample map generation with automated model fitting algorithms. This substitution of mechanical/manual processes with computational methods maintains high reliability through accurate mathematical modeling while significantly reducing the time required for sample map generation
3Manufacturing precision
If model fitting is performed for both positions and shapes, then the sample map accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the sample map generation into distinct components: first fitting models to represent field region positions, then separately fitting models to represent field region shapes. This segmentation allows each model to be optimized independently for its specific purpose, improving overall sample map accuracy while managing computational complexity through modular processing
Solution Approach 2:
The patent implements local quality by applying different model fitting approaches for different aspects of field region representation. Position models and shape models use appropriate mathematical representations tailored to their specific functions, ensuring high precision for each local requirement without unnecessarily complicating the entire system
Data Source
AI summary
Methods are disclosed for generating a sample map and processing a sample. In one arrangement, a method comprises measuring a position of a first mark in each of a plurality of field regions on sample. A first model is fitted to the measured positions of the first marks. The fitted first model represents positions of the field regions. The method comprises measuring positions of a plurality of second marks in one field region or in each of a plurality of field regions. A second model is fitted to the measured positions of the second marks. The fitted second model represents a shape of each field region. A sample map is output using the fitted first and second models.