Extraction of Signals from Noise.

Atom lithography with chromium can be utilized to fabricate a pitch standard, which is directly traceable to the wavelength of the laser standing waves. The result of a calibrated commercial AFM measurement demonstrates that the pitch standard is (212.8±0.1) nm with a peak-to-valley-height (PTVH) better than 20 nm. The measurement results show the high period accuracy of traceability with the standing laser wavelength (λ/2 = 212.78 nm). The Cr nano-grating covers a 1000 μm×500 μm area, with a PTVH better than 10 nm. The feature width broadening of the Cr nanostructure has been experimentally observed along the direction of the standing waves. The PTVH along the Gaussian laser direction is similar to a Gaussian distribution. Highly uniform periodic nanostructures with a big area at the millimeter scale, and the surface growth uniformity of the Cr nano-grating, show its great potential in the application of a traceable pitch standard at trans-scales.

https://iopscience.iop.org/article/10.1088/1674-1056/23/9/090601/pdf

Fabrication and measurement of traceable pitch standard with a big area at trans-scale

\n\nAs progress on the nanofabrication has made semiconductor developed\nrapidly, there is an increasing need in precise pitch standards to calibrate the structure of devices at\nnanoscale. Nano-gratings fabricated by atom lithography are unique and suitable to act as precise\npitch standard because its pitch distance is directly traceable to a natural constant. As the scaling\ndown of nano-devices, it is very challenging to double the spatial frequency of nano-grating while\nkeeping the self-traceability in atom lithography.\n\n\n\nIn this study, the switching-detuning light mask is utilized for Cr atom lithography. During\na single deposition process, the standing wave frequency is switching from positive detuning to negative\ndetuning alternatively.\n\n\n\nNano-gratings fabricated using switching-detuning light mask is successfully replicated\nwith double spatial frequency and self-traceability. Non-uniformity between neighboring Cr lines\nshows up with a corrected pitch of 107.15\n\n\n\nNon-uniformity is mainly caused by the dipole force discrepancy between positive and\nnegative detuning light mask. Therefore, to increase the high uniformity of nano-gratings, the deposition\ntime of negative detuning should be at least twice as the positive detuning. On the other hand, to\nreduce the pitch uncertainty, it is necessary to reduce the distance between the atom beam and reflection\nmirror as close as possible. These two significant optimization designs are promising to increase\nthe spatial frequency doubling performance with high uniformity and accuracy.\n

Deposition of Cr Atoms Using Switching-Detuning Light Mask for Direct Atom Lithography

The recent detection of gravitational waves is a remarkable milestone in the history of astrophysics. With the further development of gravitational wave detection technology, traditional filter-matching methods no longer meet the needs of signal recognition. Thus, it is imperative that we develop new methods. In this study, we apply a gravitational wave signal recognition model based on Fourier transformation and a convolutional neural network (CNN). The gravitational wave time-domain signal is transformed into a 2D frequency-domain signal graph for feature recognition using a CNN model. Experimental results reveal that the frequency-domain signal graph provides a better feature description of the gravitational wave signal than that provided by the time-domain signal. Our method takes advantage of the CNN's convolution computation to improve the accuracy of signal recognition. The impact of the training set size and image filtering on the performance of the developed model is also evaluated. Additionally, the Resnet101 model, developed on the Baidu EasyDL platform, is adopted as a comparative model. Our average recognition accuracy performs approximately 4% better than the Resnet101 model. Based on the excellent performance of convolutional neural network in the field of image recognition, this paper studies the characteristics of gravitational wave signals and obtains a more appropriate recognition model after training and tuning, in order to achieve the purpose of automatic recognition of whether the signal data contain real gravitational wave signals.

/pdf/gravitational-wave-signal-recognition-model-based-on-fourier-t1j7c9t6.pdf

Gravitational Wave-Signal Recognition Model Based on Fourier Transform and Convolutional Neural Network

Method of identifying the relative position between standing wave of laser light and substrate in atom lithography

The Data Analysis in Gravitational Wave Detection

Gravitational Waves and Gravitational-wave Sourcestwo

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Method of identifying the relative position between standing wave of laser light and substrate in atom lithography

The Data Analysis in Gravitational Wave Detection

Gravitational Waves and Gravitational-wave Sourcestwo