Phase Correction in Lissajous MEMS Scanners
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Solution Overview
Problem
Phase shifting during Lissajous scanning with MEMS scanners, caused by environmental perturbations and material property changes, leads to image blurriness and double-image effects, making it difficult to achieve accurate image reconstruction, especially in miniaturized devices like endoscopes where manual phase adjustment is impractical.
Innovation Solution
A computer-implemented method using threshold-based and variance-based metrics to predict and correct phase drift by analyzing raw data sets, identifying extremum points, and generating reconstructed images, which includes a system with processors and scanners to sequentially sample objects and execute algorithms for phase correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If Lissajous scanning is used with MEMS scanners to achieve large field of view and high frame rate, then scanning efficiency and imaging speed are improved, but phase drift occurs due to environmental perturbations and material property changes, leading to image blurriness and reconstruction errors
Solution Approach 1:
The patent implements a feedback mechanism where the measured scan trajectory from raw data is compared against the expected Lissajous pattern, and phase corrections are applied based on the detected deviations. This closed-loop approach continuously compensates for phase drift, maintaining image reconstruction accuracy despite environmental perturbations and material property changes in MEMS scanners.
Solution Approach 2:
The patent replaces manual mechanical phase adjustment mechanisms with an automated computational approach. Instead of physically adjusting scanner components to correct phase drift, the system uses image processing algorithms to detect trajectory deviations and applies digital phase corrections to the raw data, enabling automatic compensation without mechanical intervention.
2Manufacturing precision
If manual phase adjustment is used to correct phase drift, then image reconstruction accuracy can be improved, but the complexity and impracticality increases for miniaturized devices like endoscopes
Solution Approach 1:
The patent implements a self-service system where the imaging device automatically detects and corrects its own phase drift without external intervention. The system uses the acquired raw data to measure scan trajectory, identify phase deviations, and apply corrections autonomously, eliminating the need for manual adjustment by operators and making the device practical for miniaturized applications like endoscopes.
Solution Approach 2:
The patent introduces an intermediary computational layer between the physical scanning process and image reconstruction. This intermediary processing stage analyzes raw data to detect phase drift and generates correction parameters, acting as a mediator that translates physical scanner behavior into appropriate phase adjustments without requiring direct manual intervention.
3Area of stationary object
If resonant frequency operation is used to achieve large field of view, then scanning coverage is improved, but phase drift increases due to sensitivity to environmental conditions and material property changes
Solution Approach 1:
The patent employs feedback control where the actual scan trajectory measured from raw data is continuously monitored and compared against the ideal Lissajous pattern. Phase corrections are computed based on detected deviations and applied to subsequent scans, creating a closed-loop system that maintains phase stability despite resonant operation sensitivity to environmental conditions and material property changes.
Solution Approach 2:
The patent dynamically adjusts scanning parameters including phase offsets based on measured trajectory deviations. By changing operational parameters in response to detected phase drift, the system maintains reliable image reconstruction accuracy while operating at resonant frequencies for large field of view coverage.
Data Source
AI summary
A computer-implemented method includes receiving a raw data set representing an image of a sample, identifying a first set of extremum by analyzing the raw data set using a metric algorithm, identifying a second set of extremum by analyzing the first set of extremum, and generating, based on the second set of extremum, a reconstructed image of the sample. A phase correcting scanner includes one or more processors, one or more scanner adapted to sequentially sample an object to generate a vector of raw data representing the object in a Lissajous pattern, and memory storing instructions that, when executed by the one or more processors, cause the computing system to receive the vector of raw data, identify a first set of extremum by analyzing the raw data set using a metric algorithm, and identify a second set of extremum by analyzing the first set of extremum.