Understanding and accurately analyzing the mechanical properties of microelectromechanical systems (MEMS) is essential for assessing their device functionality and performance. Precisely measuring the dynamic properties of a quartz tuning fork (QTF), for instance, can significantly enhance the imaging resolution of QTF-based scanning probe microscopy systems. Current methods, such as analytical and numerical approaches, have limitations when it comes to providing accurate measurements. To overcome these limitations, we proposed an experimental approach that combines stroboscopic and sampling Moiré (SM) techniques. Our method focuses on investigating the in-plane vibration behavior of a QTF and utilizes the obtained results to measure its mechanical parameters. To achieve this, we synchronize nanometer-scale light pulses, generated using a custom-designed stroboscope, with the QTF’s excitation voltage, effectively freezing the vibrations. These vibrations are then observed using a standard CCD camera. Subsequently, SM analysis is employed to extract the surface vibration profile, facilitating the measurement of dynamic properties. This technique has the potential to analyze various micro-devices that are compatible with the sample preparation process.
Single-pixel imaging, which allows imaging with a single-pixel detector and a correlation method, can be accelerated by combining machine learning. In addition, the accuracy of the estimation was improved using the uncertainty of the estimated value by machine learning. The machine-learning algorithm was constructed from a physical perspective based on errors in the measurement system. On the other hand, to improve the reliability of the machine learning estimates, the uncertainty of the estimates was evaluated using standard deviation values derived by data augmentation. By using the value with the lowest uncertainty as the final estimate, we improved machine learning and achieved measurements with a small number of illuminations.
Recently, there has been a high demand for high-quality imaging from weak light in measuring micro-defects. Deep Learning Ghost Imaging (DLGI) has been proposed as a fast and sensitive imaging method for defect inspection. However, measurement with deep learning has a problem evaluating the prediction uncertainty. The predicted value from deep learning is distributed close to the true value in the feature, while the traditional measurement value is physically distributed close to the true value. Then, applying the conventional uncertainty evaluation method based on statistics is difficult. To overcome this problem, we propose the evaluation method of the prediction uncertainty based on the feature map in the middle layer of the CNN. By adding random numbers to the middle layer, several close estimates of feature values can be obtained. The standard deviation of these estimates is defined as prediction uncertainty. This paper shows the numerical comparison of the proposed method with evaluation by data augmentation, which evaluates the prediction uncertainty by adding fluctuations to the input data. The data augmentation method can estimate the uncertainty of changes in measurement conditions. Although the data augmentation method does not provide enough change for low SNR data, which makes uncertainty evaluation difficult, the proposed method offers constant fluctuation even for low SNR data. We have numerically confirmed that the proposed method can accurately evaluate the prediction uncertainty even for low SNR.
Recently, there has been considerable interest in mass production technology of metal three-dimensional periodic nanostructures. For mass production, photolithography using diffraction phenomenon is well suited expect the disadvantage that it is difficult to fabricate the metal structures. Therefore, this paper reports for the first time a fabrication of the metal three-dimensional periodic nanostructures, by the diffraction-based lithography using metal ion-containing photoresists. In this report, Ag was used as a metal material to fabricate the structures. An examination of Ag+ concentration shows that for Ag+ equal to 0.28% or less, Ag nanoparticles are not formed on the photoresist. Under these conditions, it is found that the optimum exposure for fabricating the structures is 400 mJ/cm2.
The demand for sub-nanometer resolution of surface roughness measurement is highly increasing in many fields as this property has a significant impact for the characteristic of a material. Besides having sub-nanometer resolution, other preferable traits for the instruments are being non-contact and high-speed measurement. Ellipsometry is an instrument with high sensitivity to material’s characteristics through the measurement of polarization of light. In this paper, an ellipsometry based on the spin Hall effect of light is proposed for sub-nanometer surface area measurement. A modified weak measurement is incorporated in the measurement model to achieve better accuracy. Performance comparison of the modified weak measurement is carried through 2D surface measurement of an optical flat which shows how it improves the measurement result of the SHEL ellipsometry and demonstrates its potential for precision measurement.
In demand for minute defect inspection, it is required to detect weak scattered light caused by small defects. Ghost imaging (GI) is
known for its high sensitivity and high noise resistance method. However, it requires many measurements to obtain a high-quality image
because GI is the correlation-based imaging method. Reducing the number of measurements, a method combined with deep learning has
been proposed. In order to improve the estimation accuracy using CNN, we propose to parallelize the convolutional layers. Parallel
convolutional layers can efficiently extract both local and global features, which contributes to the improvement of estimation accuracy.
In this report, we show that parallel CNN is more accurate than conventional CNN by experiments.
In recent years, unique functions of nano-periodic structures have attracted much attention, and there is a need for
processing techniques with high processing efficiency and flexibility. Therefore, we focused on Talbot lithography, which
has excellent processing efficiency for periodic structures. In this paper, we use hologram-assisted Talbot lithography to
improve the processing flexibility of Talbot lithography. Hologram-assisted Talbot lithography is a method to improve
the processing flexibility of structures by using CNN to estimate the incident light distribution. In order to improve the
accuracy of the hologram-assisted Talbot lithography method for controlling periodic structures, we studied the learning
of CNN. And we showed that the shape and period of the structure can be controlled by using CNN.
We propose a novel imaging method using the Ghost Imaging (GI) in a photon limited imaging. In this report, in order
to obtain image using few photons, the First Photon-detection Time (FPT) is applied to the GI. The proposed signal detection
method is able to obtain a signal with only 1 photon. The FPT has variations that are due to a shot noise. Using a correlation,
effect of the noise on images is removed. As a result, the GI with FPT (FPGI) was able to obtain high quality image than a
conventional imaging method using same photon number. Furthermore, to improve the detection time, we modified
machine learning to reduce the measurement number in the view point of noise influence.
This paper reports for high-sensitivity imaging based on Ghost imaging (GI), which is one of the single-pixel imaging. Although the GI is correlation-based imaging between structured illumination lights and detected signals, there is an advantage in detecting weak light intensity such as fluorescence of molecules. Especially, in the case of using extream weak light intensity, a photon signal is useful for imaging. Therefore, we focused on the arrival time of the first photon and used the time as the intensity of the signal. Furthermore, to improve the detection time, we applied machine learning to reduce the measurement number. In this paper, we have proposed the principle and some experimental results.
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