Modeling of the MEMS Reactive Ion Etching Process Using Neural Networks

Abstract Reactive ion etch (RIE) is commonly used in microelectromechanical systems (MEMS) fabrication as plasma etching method, where ions react with wafer surface substrate in plasma environment. Due to the importance of RIE in the MEMS field, two prediction models are established to predict the wafer status in reactive ion etching process: back-propagation neural network (BPNN) and principle component analysis BPNN (PCABPNN). These models have the potential to reduce the overall cost of ownership of MEMS equipment by increasing the wafer yield, and not depend upon monitoring wafers or expensive metrology rather it will enable inexpensive real-time wafer-to-wafer control applications in RIE. The artificial neural net (ANN) is trained with historical available input-output process data. Once trained, the ANN forecasts the process output rapidly if given the input values

Electronic sensor technologies in monitoring quality of tea: A review

Tea, after water, is the most frequently consumed beverage in the world. The fermentation of tea leaves has a pivotal role in its quality and is usually monitored using the laboratory analytical instruments and olfactory perception of tea tasters. Developing electronic sensing platforms (ESPs), in terms of an electronic nose (e-nose), electronic tongue (e-tongue), and electronic eye (e-eye) equipped with progressive data processing algorithms, not only can accurately accelerate the consumer-based sensory quality assessment of tea, but also can define new standards for this bioactive product, to meet worldwide market demand. Using the complex data sets from electronic signals integrated with multivariate statistics can, thus, contribute to quality prediction and discrimination. The latest achievements and available solutions, to solve future problems and for easy and accurate real-time analysis of the sensory-chemical properties of tea and its products, are reviewed using bio-mimicking ESPs. These advanced sensing technologies, which measure the aroma, taste, and color profiles and input the data into mathematical classification algorithms, can discriminate different teas based on their price, geographical origins, harvest, fermentation, storage times, quality grades, and adulteration ratio. Although voltammetric and fluorescent sensor arrays are emerging for designing e-tongue systems, potentiometric electrodes are more often employed to monitor the taste profiles of tea. The use of a feature-level fusion strategy can significantly improve the efficiency and accuracy of prediction models, accompanied by the pattern recognition associations between the sensory properties and biochemical profiles of tea

A review of data mining applications in semiconductor manufacturing

The authors acknowledge Fundacao para a Ciencia e a Tecnologia (FCT-MCTES) for its financial support via the project UIDB/00667/2020 (UNIDEMI).For decades, industrial companies have been collecting and storing high amounts of data with the aim of better controlling and managing their processes. However, this vast amount of information and hidden knowledge implicit in all of this data could be utilized more efficiently. With the help of data mining techniques unknown relationships can be systematically discovered. The production of semiconductors is a highly complex process, which entails several subprocesses that employ a diverse array of equipment. The size of the semiconductors signifies a high number of units can be produced, which require huge amounts of data in order to be able to control and improve the semiconductor manufacturing process. Therefore, in this paper a structured review is made through a sample of 137 papers of the published articles in the scientific community regarding data mining applications in semiconductor manufacturing. A detailed bibliometric analysis is also made. All data mining applications are classified in function of the application area. The results are then analyzed and conclusions are drawn.publishersversionpublishe

Computerized model to forecast low-cost housing demand in urban area in Malaysia using Artificial Neural Networks (ANN)

The forecasted proportions of urban population to total population in Malaysia are steadily increasing from 26% in 1965 to 70% in 2020. Therefore, there is a need to fully appreciate the legacy of the urbanization of Malaysia by providing affordable housing. The main aim of this study is to focus on developing a model to forecast the demand of low cost housing in urban areas. The study is focused on eight states in Peninsular Malaysia, as most of these states are among the areas predicted to have achieved the highest urbanization level in the country. The states are Kedah, Penang, Perlis, Kelantan, Terengganu, Perak, Pahang and Johor. Monthly time-series data for six to eight years of nine indicators including: population growth; birth rate; child mortality rate; unemployment rate; household income rate; inflation rate; GDP; poverty rate and housing stocks have been used to forecast the demand on low cost housing using Artificial Neural Network (ANN) approach. The data is collected from the Department of Malaysian Statistics, the Ministry of Housing and the Housing Department of the State Secretary. The Principal Component Analysis (PCA) method has been adopted to analyze the data using SPSS 18.0 package. The performance of the Neural Network is evaluated using R squared (R2) and the accuracy of the model is measured using the Mean Absolute Percentage Error (MAPE). Lastly, a user friendly interface is developed using Visual Basic. From the results, it was found that the best Neural Network to forecast the demand on low cost housing in Kedah is 2-16-1, Pahang 2-15-1, Kelantan 2-25-1, Terengganu 2-30-1, Perlis 3-5-1, Pulau Pinang 3-7-1, Johor 3-38-1 and Perak 3-24-1. In conclusion, the evaluation performance of the model through the MAPE value shows that the NN model can forecast the low-cost housing demand very good in Pulau Pinang, Johor, Pahang and Kelantan, where else good in Kedah and Terengganu while in Perlis and Perak it is not accurate due to the lack of data. The study has successfully developed a user friendly interface to retrieve and view all the data easily

Aislamiento de fallos por estimación de parámetros con técnicas neuronales

XXV Jornadas de Automática,Ciudad Real, 8, 9, y 10 de septiembre de 2004El artículo aborda un novedoso método para la estimación de parámetros inherentes a procesos no lineales y de difícil medida utilizando masivamente redes neuronales entrenadas mediante propagación hacia atrás para ser utilizado en la detección y aislamiento de fallos en procesos físicos. La herramienta para la utilización masiva de redes neuronales es una parte de DeltaV y permite su implementación dentro de arquitecturas de control basadas en el estándar Foundation Fieldbu

Artificial Intelligence and Industry 4.0

Cílem této práce je poskytnout přehled aplikací metod umělé inteligence v kontextu průmyslu 4.0. První kapitola je věnována definici konceptu průmyslu 4.0, předchozímu vývoji průmyslu a zařazení vědního oboru umělé inteligence do tohoto konceptu. Druhá kapitola je zaměřena na rešerši aplikací metod umělé inteligence v oblasti obrábění, výrobního průmyslu, automatizace a energetiky. Závěr práce je věnován zhodnocení metod, jejich výhod a úskalí z pohledu jednotlivých praktických aplikací a zmiňuje možné směry budoucího vývoje.The aim of this work is to provide an overview of the application of artificial intelligence methods in the context of Industry 4.0. The first chapter defines the concept of industry 4.0, previous development of the industry and inclusion of the scientific field of artificial intelligence in this concept. The second chapter is focused on the applications of artificial intelligence methods in the field of machining, manufacturing industry, automation and energetics. The work concludes with evaluation of methods, their advantages and disadvantages from the point of view of individual practical applications and mentions possible directions of future development.

Tea Category Identification Using a Novel Fractional Fourier Entropy and Jaya Algorithm

This work proposes a tea-category identification (TCI) system, which can automatically determine tea category from images captured by a 3 charge-coupled device (CCD) digital camera. Three-hundred tea images were acquired as the dataset. Apart from the 64 traditional color histogram features that were extracted, we also introduced a relatively new feature as fractional Fourier entropy (FRFE) and extracted 25 FRFE features from each tea image. Furthermore, the kernel principal component analysis (KPCA) was harnessed to reduce 64 + 25 = 89 features. The four reduced features were fed into a feedforward neural network (FNN). Its optimal weights were obtained by Jaya algorithm. The 10 × 10-fold stratified cross-validation (SCV) showed that our TCI system obtains an overall average sensitivity rate of 97.9%, which was higher than seven existing approaches. In addition, we used only four features less than or equal to state-of-the-art approaches. Our proposed system is efficient in terms of tea-category identification

Using multiple feature spaces-based deep learning for tool condition monitoring in ultra-precision manufacturing

Tool condition monitoring is critical in ultra-precision manufacturing in order to optimize the performance of the overall process, while maintaining the desired part quality. Recently, Deep Learning has been successfully applied in numerous classification tasks in manufacturing, often to forecast part quality. In this paper, a novel Deep Learning data-driven modeling framework is presented, which includes fusion of multiple stacked sparse autoencoders for tool condition monitoring in ultra-precision machining. The proposed computational framework consists of two main structures. A training model that is designed with the ability to process multiple parallel feature spaces to learn the lower-level features; and a feature fusion structure that is used to learn the higher-level features and associations to tool wear. To achieve this learning structure, a modified loss function is utilized that enhances the feature extraction and classification tasks. A dataset from a real manufacturing process is used to demonstrate the performance of the proposed framework. Experimental results and simulations show that the proposed method successfully classifies the ultra-precision machining case study with over 96% accuracy, while also outperforms comparable methodologies