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The electronics industries are relying increasingly on robotics for their production. Wafer handling robots are used to transfer wafers between wafer processing stations. A pick‐measure‐place method is typically utilized to transfer wafers accurately. The measurement step is performed using an aligner, which is time‐consuming. To increase wafer transfer efficiency, it is desirable to speed up the measurement process or place it in parallel with other operations. To solve the problem, optic sensors are installed at each station to estimate the wafer eccentricity on‐the‐fly. The eccentricity values are then applied to control the robot to place the wafer directly onto another station accurately without using the aligner. However, current methods face problems to achieve high accuracy requirements to meet the electronic manufacturing needs. The purpose of this paper is to develop a technique to improve the wafer handling performance in semiconductor manufacturing.

The kinematics model of the wafer handling robot is developed. Two sensor location calibration algorithms are proposed. Method I is based on the wafer handling path. Method II uses the offset paths from the wafer handling path. The results from these two methods are compared. To compute the wafer eccentricity on‐the‐fly, a wafer eccentricity estimation technique is developed.

The developed methods are implemented using a wafer handling robotic system in semiconductor manufacturing. The wafer eccentricity estimation errors are greatly reduced using the developed methods. The experimental results demonstrate that Method II achieves better results and can be used to improve the wafer handling accuracy and efficiency.

The proposed technique is implemented and tested many times on a wafer handing robotic system. The notch alignment in the wafer handling needs further research.

The developed method is validated using a system in semiconductor manufacturing. Hence the developed method can be directly implemented in production if the notch of a wafer can be identified.

This paper provides techniques to improve the wafer handling accuracy in semiconductor manufacturing. Compared with the results using other methods, Method II greatly increases the wafer handling accuracy to satisfy the semiconductor manufacturing needs.

The purpose of this paper is to identify the root cause of low yield problems in the semiconductor manufacturing process using sensor data continuously collected from manufacturing equipment and describe the process environment in the equipment.

This paper proposes a sensor data mining process based on the sequential modeling of random forests for low yield diagnosis. The process consists of sequential steps: problem definition, data preparation, excursion time and critical sensor identification, data visualization and root cause identification.

A case study is conducted using real-world data collected from a semiconductor manufacturer in South Korea to demonstrate the effectiveness of the diagnosis process. The proposed model successfully identified the excursion time and critical sensors previously identified by domain engineers using costly manual examination.

The proposed procedure helps domain engineers narrow down the excursion time and critical sensors from the massive sensor data. The procedure's outcome is highly interpretable, informative and easy to visualize.

This paper aims to give an overview about the state of the art and novel technologies used in gas sensing. It also discusses the miniaturization potential of some of these technologies in a comparative way.

In this article, the authors state the most of the methods used in gas sensing discuss their advantages and disadvantages and at last the authors discuss the ability of their miniaturization comparing between them in terms of their sensing parameters like sensitivity, selectivity and cost.

In this article, the authors will try to cover most of the important methods used in gas sensing and their recent developments. The authors will also discuss their miniaturization potential trying to find the best candidate among the different types for the aim of miniaturization.

In this article, the authors will review most of the methods used in gas sensing and discuss their miniaturization potential delimiting the research to a certain type of technology or application.

Sensors based on semiconductors like silicon are becoming increasingly important as Stephen McClelland found out.

The present and potential applications of screen‐printing thick film technology to the field of chemical sensing are briefly reviewed in the first part of the paper. The second part is devoted to the search for new routes to selectivity for basically non‐selective gas sensors.

With the acceleration of global energy structure transformation, hydrogen has been widely used for its non-pollution and high efficiency, and hydrogen detection is used to guarantee the hydrogen safety. The purpose of this paper is to study the research foundation, trend and hotspots of hydrogen detection field.

A total of 4,076 literature records from 2000 to 2021 were retrieved from the core collection of the Web of Science database selected as data sources. The literature information mining was realized by using CiteSpace software. Bibliometrics was used to analyze information, such as keywords, authors, journals, institutions, countries and cited references, and to track research hotspots.

Since the 21st century, the number of publications in the hydrogen detection field has been in a stable stepped uptrend. In terms of research foundation, the hotspots such as core-shell structures, nano-hybrid materials and optical fiber hydrogen sensors have been studied extensively. In combination with the discipline structure and research frontier, the selectivity, sensitivity, response speed and other performance parameters of hydrogen sensors need further improvement. The establishment of an interdisciplinary knowledge system centered on materials science and electronic science will become a long-term trend in the research of hydrogen detection.

This study presents an overview of research status, hotspots and laws in hydrogen detection field, through the quantitative analysis of much literature in the field and the use of data mining, so as to provide credible references for the research of hydrogen detection technology.

This paper aims to study different possibilities for implementing easy-to-use and cost-effective micro-systems to detect and trace expelled gases from rotten food. The paper covers various radio-frequency identification (RFID) technologies and gas sensors as the two promoting feasibilities for the tracing of packaged food. Monitoring and maintaining quality and safety of food in transport and storage from producer to consumer are the most important concerns in food industry. Many toxin gases, even in parts per billion ranges, are produced from corrupted and rotten food and can endanger the consumers’ health. To overcome the issues, intelligent traceability of food products, specifically the packaged ones, in terms of temperature, humidity, atmospheric conditions, etc., has been paid attention to by many researchers.

Food poisoning is a serious problem that affects thousands of people every year. Poisoning food must be recognized early to prevent a serious health problem.

Contaminated food is usually detectable by odor. A small gas sensors and low-cost tailored to the type of food packaging and a communication device for transmitting alarm output to the consumer are key factors in achieving intelligent packaging.

Conducting polymer composite, intrinsically conducting polymer and metal oxide conductivity gas sensors, metal–oxide–semiconductor field-effect transistor (MOSFET) gas sensors offer excellent discrimination and lead the way for a new generation of “smart sensors” which will mould the future commercial markets for gas sensors.

Small size, low power consumption, short response time, wide operating temperature, high efficiency and small area are most important features of introduced system for using in package food.

Summarizes some of the most commonly used gas sensors and describes how each one works. Covers solid state gas sensors; aqueous electrochemical gas sensors; paramagnetic gas sensors, photometric gas sensors; thermal conductivity gas sensors and fibre‐optic gas sensors.

Discusses intelligent materials, intelligent material‐based sensors, their transducing methods, and different kinds of transducers used with smart material‐based sensors. Assumes that the future of intelligent sensors will almost totally depend on intelligent chemistry and intelligent instrumentation. Molecular recognition will widen the horizons of smart systems with the help of VLSI‐based design and fabrication. Discusses different sensor mechanisms, such as ENFETs, immunoFETs, ISFETs and chemFETs and takes a detailed look at potentiometric, amperometric and optical biosensors.