Nanometer-resolution 4-Probe Laser Interferometric Displacement Sensor Performance Evaluation: Principles and Operations
IEEE Transactions on Instrumentation and Measurement(2025)
Abstract
A 4-probe laser interferometric displacement sensor (4pLIDS) has advantages over a single-probe LIDS in a micro-thruster calibration system. By synchronously measuring the displacements at four points and differentiating the measured data, the common-mode noise can be greatly suppressed, thereby improving the measurement accuracy. Based on the design requirements of the nanometer-resolution 4pLIDS, we discussed the connections among various performance aspects of LIDS, designed evaluation schemes, and conducted tests. We devised the schemes to evaluate the 4-probe performances related to synchronicity using a single nanopositioning stage as the displacement reference, and considering the agreement and output temporal uniformity from each probe. We further analyzed the influences of the non-ideal performances of the displacement reference on the evaluation results to ensure that the results truly reveal the LIDS's performances. The evaluation results show that the resolution of the 4pLIDS reaches 0.7 nm, the standard deviation (SD) of the 4pLIDS repeatability is no more than 0.15 nm and the inconsistency error among the 4 probes is within ±0.4 nm. The actual differential measurement results using the 4pLIDS conform to the predictions from the evaluated performances and demonstrate the effectiveness of the evaluation.
MoreTranslated text
Key words
Laser interferometric displacement sensor,micro-thruster,multi-probe,performance evaluation
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined