2019
DOI: 10.1038/s41377-018-0109-7
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Probing 10 μK stability and residual drifts in the cross-polarized dual-mode stabilization of single-crystal ultrahigh-Q optical resonators

Abstract: The thermal stability of monolithic optical microresonators is essential for many mesoscopic photonic applications such as ultrastable laser oscillators, photonic microwave clocks, and precision navigation and sensing. Their fundamental performance is largely bounded by thermal instability. Sensitive thermal monitoring can be achieved by utilizing cross-polarized dual-mode beat frequency metrology, determined by the polarization-dependent thermorefractivity of a single-crystal microresonator, wherein the heter… Show more

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Cited by 383 publications
(93 citation statements)
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“…This limitation creates a trade-off between the object's refractive index contrast and overall height to provide an accurate reconstruction. Both model-based [12,[40][41][42][43][44] and machine learning-based [45][46][47][48] approaches have shown excellent results in extracting useful information from multiple scattering. We will expand mIDT to consider multiple-scattering in future work following these methods.…”
Section: Discussionmentioning
confidence: 99%
“…This limitation creates a trade-off between the object's refractive index contrast and overall height to provide an accurate reconstruction. Both model-based [12,[40][41][42][43][44] and machine learning-based [45][46][47][48] approaches have shown excellent results in extracting useful information from multiple scattering. We will expand mIDT to consider multiple-scattering in future work following these methods.…”
Section: Discussionmentioning
confidence: 99%
“…The optical images and the corresponding reconstructed images in Figure 6 b demonstrate that not only the gestures of the person but also the “see-through-the-wall” ability can be achieved by the machine-learning metamaterial cameras. The same group, as shown in Figure 6 c, further developed an intelligent metasurface convolutional neural network (IM-CNN) to reconstruct the image of a human body, to monitor the respiration from the region of interest (ROI) of the whole image, to recognize the hand gesture with additional CNN or the time-frequency analysis of the microwave data [ 114 ]. The metasurface-based CNN not only can exclude unwanted disturbances but also can enhance the SNR by a factor of 20 dB.…”
Section: Enabled Hmi Applicationsmentioning
confidence: 99%
“…Previous HMI scenarios are implemented by different kinds of sensors, but integrating these different sensors into a practical network is still challenging, such as the power supply problem and the data collection issue [ 2 , 114 , 131 , 138 ]. For convenient wearability, wireless technologies are desired to achieve the body networks, but conventional radio frequency (RF) signals may be transmitted into the ambient space, leading to the low efficiency and useless power consumption.…”
Section: Enabled Hmi Applicationsmentioning
confidence: 99%
“…In the past couple of years, the concept of deep learning has emerged as a gold-standard solution to many types of problems in endless fields [25], where the revolution lies in the use of hundreds of hidden layers, consisting of millions of parameters, which is enabled by recent computational advancements. Specifically in the field of image processing, deep convolutional neural networks have revolutionized problems ranging from basic classification [26] and segmentation [27] to complex inverse problems in imaging [28][29][30][31][32][33][34][35]. For the latter case of inverse problems, the residual neural network (ResNet) architecture [36], which adds short-term memory to each layer, has taken the lead due to its ability to force the network to learn new information in every layer beyond what is already encoded in the network.…”
Section: Introductionmentioning
confidence: 99%