Interferometry Phase Correction for Multi-Layer Stack Metrology
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Solution Overview
Problem
Metrology of multi-layer stacks, such as those used in wearable virtual and augmented reality devices, faces challenges due to undesired light reflection and dispersion effects, making it difficult to maintain high-quality image production by ensuring parallelism, flatness, and specific separation distances between plates during manufacturing.
Innovation Solution
A low coherence imaging interferometry system is used to acquire sample interferometry data, transform it into the frequency domain, identify and remove non-linear phase variations caused by dispersion, and process the compensated data to determine information about the test sample, including distance, flatness, and thickness of plates and gaps, without requiring prior knowledge of the test sample's characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional interferometry is used to measure multi-layer stacks, then measurement capability is provided, but dispersion effects and light reflection cause non-linear phase variations that degrade measurement precision
Solution Approach 1:
The patent transforms the interferometry data from spatial domain to frequency domain, identifying and removing non-linear phase variations caused by dispersion. This parameter transformation approach allows the system to compensate for dispersion effects by operating in the frequency domain where phase corrections can be applied systematically, thereby resolving the contradiction between maintaining measurement capability and eliminating dispersion-induced errors
Solution Approach 2:
The patent extracts and removes the harmful non-linear phase variation component from the interferometry data. By identifying the dispersion-induced phase errors as a separable component in the frequency domain and removing it, the system isolates the useful measurement information from the harmful dispersion effects, improving measurement precision
2Manufacturing precision
If measurement of multiple parameters (parallelism, flatness, separation distance) is performed in multi-layer stacks, then manufacturing quality is improved, but the complexity of measurement and data processing increases
Solution Approach 1:
The patent employs a universal frequency-domain processing approach that can simultaneously extract multiple manufacturing parameters (parallelism, flatness, separation distance) from the same interferometry dataset. This multi-functional processing method eliminates the need for separate measurement procedures for each parameter, reducing overall measurement complexity while maintaining manufacturing precision
Solution Approach 2:
The patent segments the complex measurement problem into distinct processing stages: data acquisition, Fourier transformation to frequency domain, identification of non-linear phase variations, removal of dispersion effects, and extraction of specific geometric parameters. This segmentation of the measurement process makes the complex task of measuring multiple parameters more manageable and systematic
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 effectively compensates for dispersion and other adverse influences, allowing for accurate measurement of surface topography and separation in multi-layer stacks, enhancing the quality of image production in VR/AR devices by improving the precision of plate alignment and separation.
Implementation Method 1
providing to an electronic processor sample interferometry data acquired for a test sample using a low coherence imaging interferometry system
Implementation Method 2
identifying a non-linear phase variation from the sample interferometry data in the frequency domain, in which the non-linear phase variation is a result of dispersion introduced into a measurement beam by the test sample
Data Source
AI summary
Techniques for removing interferometry signal phase variations caused by distortion and other effects in a multi-layer stack include: providing an electronic processor sample interferometry data acquired for the stack using a low coherence imaging interferometry system; transforming, by the electronic processor, the sample interferometry data to a frequency domain; identifying a non-linear phase variation from the sample interferometry data in the frequency domain, in which the non-linear phase variation is a result of dispersion introduced into a measurement beam by the test sample; and removing the non-linear phase variation from the sample interferometry data thereby producing compensated interferometry data.