Towards the Solution of Coating Loss Measurements Using Thermoelastic-Dominated Substrates

L. Silenzi, F. Fabrizi,M. Granata, L. Mereni, M. Montani,F. Piergiovanni, A. Trapananti,F. Travasso,G. Cagnoli

Classical and Quantum Gravity（2024）SCI 3区

Univ Camerino | Univ Urbino Carlo Bo | Univ Claude Bernard Lyon 1 | Univ Lyon

Cited 0|Views3

Abstract

The characterization of thin film parameters derives from the measurement difference between the coated and bare substrate. This method of comparison is based on the stationarity of the substrate: the characteristics of the substrate do not depend on the presence of the film. However, the thermoelastic loss of a coated substrate depends on the thermo-mechanical parameters of the film as well, which are generally unknown. When thermoelastic loss is dominant, the coating loss measurements are completely altered. In this paper, we propose a model that helps to understand the role of each material property in the thermoelasticity of layered plates, and with this we identified three possible cases in which any coating-substrate combination could be classified. In particular, we analyzed the IBS silica film deposited on silicon. Using the model, we were able to explain the experimental results and also selected a thinner substrate for future coating loss measurements. With this choice, cryogenic loss measurements on bare substrate confirm that thermoelastic loss becomes irrelevant for temperatures below 130 K-180 K, depending on the mode.

Translated text

Key words

thermoelastic dissipation,silicon,gravitational waves detectors,coatings

求助PDF

上传PDF

Bibtex

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