National Metrology Centre

High resolution at nanometer scale in physical, mechanical and electrical properties make Atomic Force Microscopy (AFM) an essential technique in fault isolation for semiconductor device failure analysis. With shrinkage in device dimension and increase in circuitry complexity, the requirements for both sample preparation and measurements at device level become more stringent. In this paper, we will use Conductive AFM as an example to systematically discuss how to overcome the challenges and enhance AFM capability with multi-modulations for advanced technology nodes.

Precipitable water vapor (PWV) has caught the interest of researchers for extensive study on weather prediction in recent times. Nevertheless, the prediction of rainfall cannot be solely reliant on a single metric, as other atmospheric factors also play a significant role. Atmospheric gradient has gained little attention in the field of meteorology, especially for the prediction of significant weather events. Moreover, the existing research on rainfall forecasting is mostly focused on an individual station rather than a larger region. This article aims to predict rain events in a certain geographical region instead of a single station, employing a novel methodology for the tropical climatic zone. Different features of the atmospheric gradient, along with PWV, have been rigorously studied in relation to rainfall to determine the potential criteria for predicting rain events over an area. Both gradient convergence and flux shift toward greater positive values even 6 h before the rain event over the rainy region, when compared to the area with no significant rain events, which further increases with the advancement of time. PWV also manifests a similar trend to the atmospheric gradient. In this article, we have proposed a novel dual-parameter algorithm with PWV and either of the two gradient features, which gives approximately 90% true detection (TD) with a much lower false alarm (FA) rate of around 22% for a region of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8 {\mathrm {^{\circ}}} \times 8 {\mathrm {^{\circ}}}$ </tex-math></inline-formula>. Furthermore, another two-layer forecasting algorithm has been established, which precisely predicts the location of rainfall for the next 6 h in the tropical region.

In single-wavelength digital holography (DH), the phase wrapping phenomenon limits the total object depth that can be measured due to the requirement for well-resolved phase fringes. To address this limitation, dual-wavelength DH is proposed, enabling measurement of much deeper objects. In single-wavelength DH, because the object depth is limited, the depth of focus (DOF) of DH's optical system at a reconstruction distance is sufficient to cover the object depth. To date, many autofocusing algorithms have been proposed to obtain a correct reconstruction distance. However, in dual-wavelength DH, because the object depth is extended, the DOF at a reconstruction distance cannot cover the extended object depth. The extended object depth can span multiple DOFs, causing partially out of focus object depth. Therefore, in dual-wavelength DH, relying solely on autofocusing algorithms for a single distance is insufficient. But extended autofocusing algorithms, which can autofocus objects through multiple DOFs, are demanded. However, there are no such extended autofocusing algorithms in dual-wavelength DH. Therefore, we propose an extended autofocusing algorithm for dual-wavelength DH based on a correlation coefficient. The proposed algorithm is able to focus the whole object depth when the depth spans multiple DOFs. Through theoretical analysis, simulations, and experiments, the necessity and effectiveness of the proposed algorithm are verified.

SummaryThis paper discusses the results of predicting the time differences between the two time & frequency labs in Singapore. Results show that the prediction can be achieved for up to 5 hours in advance with an average error of 2.5 ns. The prediction plays an important role to flag out any deviations from the normal clock behavior, which helps the backup (business continuity) lab to perform individually and with similar accuracy as that of the main lab. This will help to prepare for any unwanted conditions that could interrupt the timing services, hence improving the resiliency of the services.

The extreme precision of optical atomic clocks has led to an anticipated redefinition of the second by the International System of Units. Furthermore, accuracies pushing the boundary of 1 part in 1018 and beyond will enable new applications, such as in geodesy and tests of fundamental physics. The 1S0 to 3D1 optical transition in 176Lu+ has exceptionally low sensitivity to external perturbations, making it suitable for practical clock implementations with inaccuracy at or below 10−18. Here, we perform high-accuracy comparisons between two 176Lu+ references using correlation spectroscopy. A comparison at different magnetic fields is used to obtain a quadratic Zeeman coefficient of −4.89264(88) Hz/mT for the reference frequency. With a subsequent comparison at low field, we demonstrate agreement at the low 10−18 level, statistically limited by the averaging time of 42 hours. The evaluated uncertainty in the frequency difference is 9 × 10−19 and the lowest reported in comparing independent optical references.

The application of GPS technology has been pervasive in meteorological science besides point positioning for weather prediction with high spatio-temporal resolution. In meteorology, rainfall forecasting is highly imperative to mitigate the destruction of public properties and lives. In recent years, precipitable water vapor has caught the interest of the scientists in the field of research on rainfall prediction. In this article, we have presented the correlation of precipitable water vapor with time and space during the transition of weather condition from a rainy period to dry one or vice-versa. The enhancement in PWV values in a larger region prior to rainfall makes it a potential predictor for rain for a particular area. The amendment in PWV slope with time before precipitation is also significant to forecast the impending rain event. These results substantiate the usefulness of precipitable water vapor as a potential indicator of weather forecasting.

Rainfall forecasting using GPS signals has been an emerging field of research in recent decades. Several researches have been conducted on rainfall prediction based on the accumulation of water vapor. On the other hand, atmospheric gradient has gained the attention of the scientists in recent years, mostly for the application in point positioning. In this paper, we have investigated the behaviour of the gradient for different monsoon seasons in Singapore before precipitation. The variation in gradient angular estimates during both precipitation and non-rainy hours for all the four seasons provides a rainfall forecasting criteria for the tropical island Singapore. The gradient is mostly aligned with the direction of wind flow. The results bolster the fact that gradient can be a crucial parameter for the rainfall prediction along with Precipitable Water Vapor (PWV).

In recent studies, precipitable water vapor (PWV) has caught the interest of researchers in predicting rainfall. However, rainfall depends on several other atmospheric factors that play a vital role in its initiation. With only one atmospheric parameter, the false prediction is high, especially for long-term prediction. In this article, a new method for rainfall forecasting is proposed using horizontal tropospheric gradient and atmospheric residual that are important weather features. It is observed that the gradient orientation defines the weather front for a larger region, and the gradient slope, gradient magnitude, and atmospheric residual play a crucial role in rainfall prediction. The algorithm is based on global positioning system (GPS) PWV data from stations in the tropical region. This proposed algorithm obtains average false alarm (FA) and true detection (TD) rates of 36.6% and 87%, respectively, for a prediction window of 6 h. The proposed threshold values are found to be similar for three different tropical stations that make the algorithm location independent. The comparison of this approach with several other data suggests that this algorithm is suitable in the practical scenario for a long-term rainfall prediction with a better TD rate and a minimal FA rate.