MEMS Scanning Mirror Control for Temperature-Stable Raster Images
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
MEMS scanning devices exhibit unique temperature characteristics that can cause image deformation or synchronization issues at different temperatures, leading to performance degradation over time.
Innovation Solution
Building custom temperature models using machine learning to adjust control parameters based on current temperature, and updating these models over the device's lifetime through feedback from a display observing camera.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If custom temperature models are built using machine learning to adjust control parameters, then image clarity and synchronization are maintained, but device complexity increases
Solution Approach 1:
The patent implements a feedback mechanism where a display observing camera captures images of the projected display, and machine learning algorithms analyze these images to detect temperature-induced deformations. The system automatically adjusts control parameters based on this feedback, creating a closed-loop system that maintains image quality while adapting to temperature changes.
Solution Approach 2:
The system performs self-calibration by automatically building and updating temperature models using machine learning. The display observing camera and processing system work autonomously to characterize temperature effects and adjust control parameters without manual intervention, enabling the device to self-optimize performance across temperature variations.
2Reliability
If temperature models are updated over device lifetime using display observing camera feedback, then performance consistency is improved, but measurement and detection difficulty increases
Solution Approach 1:
The display observing camera provides continuous feedback about the actual display output, which is fed into machine learning algorithms to detect and characterize temperature-induced performance degradation. This feedback loop enables the system to automatically update temperature models and maintain performance consistency throughout the device lifetime.
Solution Approach 2:
The patent replaces manual measurement and calibration methods with automated machine learning-based detection. Instead of physical measurement techniques, the system uses image processing and machine learning algorithms to detect temperature effects and update models, simplifying the measurement process while improving precision.
Data Source
AI summary
A MEMS scanning device (“Device”) includes at least (1) laser projector(s) controlled by a laser drive to project a laser beam, (2) MEMS scanning mirror(s) controlled by a MEMS drive to scan the laser beam to generate a raster scan, (3) a display configured to receive the raster scan, (4) a thermometer configured to detect a current temperature, (5) a display observing camera configured to capture an image of a predetermined area of the display, and (6) a computer-readable media that stores temperature model(s), each of which is custom-built using machine learning. The device uses the display observing camera to capture image(s) of predetermined pattern(s), which are then used to extract feature(s). The extracted feature(s) are compared with ideal feature(s) to identify a discrepancy. When the identified discrepancy is greater than a threshold, the temperature model(s) are updated accordingly.