Use of Optical Emission Spectroscopy Data for Fault Detection of Mass Flow Controller in Plasma Etch Equipment

To minimize wafer yield losses by misprocessing during semiconductor manufacturing, faster and more accurate fault detection during the plasma process are desired to increase production yields. Process faults can be caused by abnormal equipment conditions, and the performance drifts of the parts or components of complicated semiconductor fabrication equipment are some of the most unnoticed factors that eventually change the plasma conditions. In this work, we propose improved stability and accuracy of process fault detection using optical emission spectroscopy (OES) data. Under a controlled experimental setup of arbitrarily induced fault scenarios, the extended isolation forest (EIF) approach was used to detect anomalies in OES data compared with the conventional isolation forest method in terms of accuracy and speed. We also used the OES data to generate features related to electron temperature and found that using the electron temperature features together with equipment status variable identification data (SVID) and OES data improved the prediction accuracy of process/equipment fault detection by a maximum of 0.84%.

Differentiable Programming of Isometric Tensor Networks

Differentiable programming is a new programming paradigm which enables large scale optimization through automatic calculation of gradients also known as auto-differentiation. This concept emerges from deep learning, and has also been generalized to tensor network optimizations. Here, we extend the differentiable programming to tensor networks with isometric constraints with applications to multiscale entanglement renormalization ansatz (MERA) and tensor network renormalization (TNR). By introducing several gradient-based optimization methods for the isometric tensor network and comparing with Evenbly-Vidal method, we show that auto-differentiation has a better performance for both stability and accuracy. We numerically tested our methods on 1D critical quantum Ising spin chain and 2D classical Ising model. We calculate the ground state energy for the 1D quantum model and internal energy for the classical model, and scaling dimensions of scaling operators and find they all agree with the theory well.

Stability analysis of the anti-lock braking system with time delay

A time delay exists between driver input and vehicle braking state response during the working process of the anti-lock braking system (ABS), and the braking performance of vehicles will be further reduced due to the delay of controllers. This paper investigates a systematic method of stability analysis for time delay ABS, and the analysis focuses on the stability and critical delay algorithm of ABS with delay time. Firstly, the dynamic structure and modelling process of ABS are briefly introduced, and PD control algorithm is adopted to improve the control performance. Then, dynamic models of ABS with time delay are derived, and the full delay stability interval and critical time delay algorithm of ABS are deduced by using the generalized Sturm criterion method. Finally, the validity of the critical delay algorithm by the proposed method and the stability and accuracy of ABS with time delay, different road conditions, vehicle speeds and control parameters are illustrated by numerical simulations, and the results show that the critical time delay algorithm of ABS can be verified under different conditions.

Structural development of a flexible textile-based thermocouple temperature sensor

Textiles are conventionally utilized as the raw materials for making clothing and complementary accessories. To keep abreast of the times, a new direction of integrating textiles into electronic technology has been given in order to develop a temperature-sensing device with outstanding built-in flexibility, versality and softness. In this study, a flexible construction of the textile-based thermocouple temperature sensor via an industrial-and-technological-based weaving process was designed. The feasible arrangement of the conductive textile materials in the warp and weft directions related to the temperature-sensing ability was studied in detail, and significant linearity was shown in the range of 5–50[Formula: see text] with different groups of combinations of the conductive yarns. More cross-intersections and ‘hot junctions’ resulted from the 3 × 3 warp–weft arrangement, offering higher stability and accuracy in thermal sensation. Besides, the resistance of the thermocouple remained almost constant under different degrees of bending. The relationship between the resistance and the bending flexibility was also investigated over a range of temperature.

Anxiety and depressive symptoms in college students during the late stage of the COVID-19 outbreak: a network approach

Mental health problems are common in college students even in the late stage of the coronavirus disease 2019 (COVID-19) outbreak. Network analysis is a novel approach to explore interactions of mental disorders at the symptom level. The aim of this study was to elucidate characteristics of depressive and anxiety symptoms network in college students in the late stage of the COVID-19 outbreak. A total of 3062 college students were included. The seven-item Generalized Anxiety Disorder Scale (GAD-7) and nine-item Patient Health Questionnaire (PHQ-9) were used to measure anxiety and depressive symptoms, respectively. Central symptoms and bridge symptoms were identified based on centrality and bridge centrality indices, respectively. Network stability was examined using the case-dropping procedure. The strongest direct relation was between anxiety symptoms “Nervousness” and “Uncontrollable worry”. “Fatigue” has the highest node strength in the anxiety and depression network, followed by “Excessive worry”, “Trouble relaxing”, and “Uncontrollable worry”. “Motor” showed the highest bridge strength, followed by “Feeling afraid” and “Restlessness”. The whole network was robust in both stability and accuracy tests. Central symptoms “Fatigue”, “Excessive worry”, “Trouble relaxing” and “Uncontrollable worry”, and critical bridge symptoms “Motor”, “Feeling afraid” and “Restlessness” were highlighted in this study. Targeting interventions to these symptoms may be important to effectively alleviate the overall level of anxiety and depressive symptoms in college students.

A Control Algorithm for Sea–Air Cooperative Observation Tasks Based on a Data-Driven Algorithm

There is tremendous demand for marine environmental observation, which requires the development of a multi-agent cooperative observation algorithm to guide Unmanned Surface Vehicles (USVs) and Unmanned Aerial Vehicles (UAVs) to observe isotherm data of the mesoscale vortex. The task include two steps: firstly, USVs search out the isotherm, navigate independently along the isotherm, and collect marine data; secondly, a UAV takes off, and in its one round trip, the UAV and USVs jointly perform the task of the UAV reading the observation data from USVs. In this paper, aiming at the first problem of the USV following the isotherm in an unknown environment, a data-driven Deep Deterministic Policy Gradient (DDPG) control algorithm is designed that allows USVs to navigate independently along isotherms in unknown environments. In addition, a hybrid cooperative control algorithm based on a multi-agent DDPG is adopted to solve the second problem, which enables USVs and a UAV to complete data reading tasks with the shortest flight distance of the UAV. The experimental simulation results show that the trained system can complete this tas, with good stability and accuracy.

A New Type of 5G-Oriented Integrated BDS/SON High-Precision Positioning

China is promoting the construction of an integrated positioning, navigation, and timing (PNT) systems with the BeiDou Navigation Satellite System (BDS) as its core. To expand the positioning coverage area and improve the positioning performance by taking advantage of device-to-device (D2D) and self-organizing network (SON) technology, a BDS/SON integrated positioning system is proposed for the fifth-generation (5G) networking environment. This system relies on a combination of time-of-arrival (TOA) and BeiDou pseudo-range measurements to effectively supplement BeiDou signal blind spots, expand the positioning coverage area, and realize higher precision in continuous navigation and positioning. By establishing the system state model, and addressing the single-system positioning divergence and insufficient accuracy, a robust adaptive fading filtering (RAF) algorithm based on the prediction residual is proposed to suppress gross errors and filtering divergence in order to improve the stability and accuracy of the positioning results. Subsequently, a federated Kalman filtering (FKF) algorithm operating in fusion-feedback mode is developed to centrally process the positioning information of the combined system. Considering that the prediction error can reflect the magnitude of the model error, an adaptive information distribution coefficient is introduced to further improve the filtering performance. Actual measurement and significance test results show that by integrating BDS and SON positioning data, the proposed algorithm realizes robust, reliable, and continuous high precision location services with anti-interference capabilities and good universality. It is applicable in scenarios involving unmanned aerial vehicles (UAVs), autonomous driving, military, public safety and other contexts and can even realize indoor positioning and other regional positioning tasks.

LBTrace: A LoRa and Blockchain Based Contact Tracing Method for COVID-19 and Future Pandemics

COVID-19 has caused hundreds of millions of infections and hundreds of deaths, and even though vaccinations are increasing, the mutation of the virus makes the pandemic even difficult to control. Existing manual, operator and Bluetooth-based technologies for epidemiological investigation and close contact tracing suffer from high cost, low accuracy, and difficulty in scaling up. Viruses such as Delta variants have a greater ability to survive and spread, making many of the existing human-human close contacts tracing less effective. Also, it is easy to overlook the fact that there is still a large segment of the world's population that does not have access to the Internet and is proficient in using smartphones, which makes the performance of smart device-based tracing much less effective. Inspired by Health Code and Tracetogether, which have been widely accepted in China and Singapore, we propose a LoRa and blockchain-based contact tracing method LBTrace, which is low-power, lightweight, and operation-free. The experimental results demonstrate the high stability and accuracy of our proposed method, which can be used as a complement to existing methods to help some governments effectively control COVID-19 and future outbreaks under certain emergency conditions.

LBTrace: A LoRa and Blockchain Based Contact Tracing Method for COVID-19 and Future Pandemics

COVID-19 has caused hundreds of millions of infections and hundreds of deaths, and even though vaccinations are increasing, the mutation of the virus makes the pandemic even difficult to control. Existing manual, operator and Bluetooth-based technologies for epidemiological investigation and close contact tracing suffer from high cost, low accuracy, and difficulty in scaling up. Viruses such as Delta variants have a greater ability to survive and spread, making many of the existing human-human close contacts tracing less effective. Also, it is easy to overlook the fact that there is still a large segment of the world's population that does not have access to the Internet and is proficient in using smartphones, which makes the performance of smart device-based tracing much less effective. Inspired by Health Code and Tracetogether, which have been widely accepted in China and Singapore, we propose a LoRa and blockchain-based contact tracing method LBTrace, which is low-power, lightweight, and operation-free. The experimental results demonstrate the high stability and accuracy of our proposed method, which can be used as a complement to existing methods to help some governments effectively control COVID-19 and future outbreaks under certain emergency conditions.