Image-Based Signal Response Metrology for Semiconductor Wafer Characterization
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Solution Overview
Problem
Current image-based metrology systems lack sufficient resolution to measure complex three-dimensional structures in semiconductor manufacturing, and traditional methods are time-consuming and limited in throughput, especially when dealing with increasingly small features and high-value wafer areas.
Innovation Solution
The development of an image-based signal response metrology (SRM) model that uses raw image data to estimate structural parameters, trained on Design of Experiments (DOE) data, allowing for direct calculation of parameters from measured image data and reducing errors and computation time, while also transforming images into synthetic non-imaging signals for faster measurement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If imaging-based measurement systems are used to characterize large wafer areas in parallel, then productivity is improved, but measurement precision deteriorates due to insufficient resolution for complex three-dimensional structures
Solution Approach 1:
The patent segments the measurement process into two distinct stages: (1) an imaging-based screening stage that rapidly identifies regions of interest across large wafer areas, and (2) a focused measurement stage that applies high-precision non-imaging techniques only to the identified regions. This segmentation allows the system to maintain high productivity during screening while achieving high measurement precision in the focused analysis phase, resolving the contradiction between throughput and accuracy.
Solution Approach 2:
The patent introduces an intermediary computational model that bridges imaging-based measurements and non-imaging model-based measurements. The model is trained using paired imaging and non-imaging data, learning to translate imaging measurements into accurate structural parameter estimates. This intermediary model enables imaging-based systems to achieve measurement precision previously only attainable through non-imaging techniques, while maintaining the high throughput advantages of imaging systems.
2Measurement precision
If traditional non-imaging model-based optical metrology techniques are used to achieve high precision measurements, then measurement precision is improved, but productivity deteriorates due to sequential signal acquisition from sparsely located targets
Solution Approach 1:
The patent creates a universal measurement system that can perform both imaging-based rapid screening and non-imaging high-precision measurements using the same hardware platform. The system is designed to execute different measurement modes depending on the requirements, making it multi-functional. This universality eliminates the need for separate measurement systems and allows flexible switching between high-throughput imaging mode and high-precision non-imaging mode within a single instrument.
Solution Approach 2:
The patent applies preliminary action by using imaging-based measurements to pre-identify and prioritize regions of interest before conducting detailed non-imaging measurements. This preliminary screening step filters out areas that do not require high-precision measurement, allowing the system to focus computational and measurement resources only on critical regions. As a result, the overall productivity improves while maintaining measurement precision where needed.
3Measurement precision
If imaging-based measurement algorithms use specialized target structures to achieve reliable measurements, then measurement precision is improved, but adaptability deteriorates because algorithms cannot perform reliably with arbitrary targets or device structures
Solution Approach 1:
The patent transforms the measurement approach by changing the fundamental parameters used for analysis. Instead of relying on geometric features of specialized targets (lines, boxes, corners), the system uses spectral parameters - the wavelength-dependent reflectance characteristics of the material. This parameter change makes the measurement algorithm independent of target geometry, enabling it to accurately measure any structure type including arbitrary device structures, while maintaining high measurement precision through the material's optical fingerprint.
Data Source
AI summary
Methods and systems for combining information present in measured images of semiconductor wafers with additional measurements of particular structures within the measured images are presented herein. In one aspect, an image-based signal response metrology (SRM) model is trained based on measured images and corresponding reference measurements of particular structures within each image. The trained, image-based SRM model is then used to calculate values of one or more parameters of interest directly from measured image data collected from other wafers. In another aspect, a measurement signal synthesis model is trained based on measured images and corresponding measurement signals generated by measurements of particular structures within each image by a non-imaging measurement technique. Images collected from other wafers are transformed into synthetic measurement signals associated with the non-imaging measurement technique and a model-based measurement is employed to estimate values of parameters of interest based on the synthetic signals.