Auto Scale Research Articles

Trends in FOWLP: 600mm Panel Level for 6-Sided Die Protection with M-Series

The fastest adoption of Fan-Out technology is now in 5G, automotive and healthcare. Traditional applications such as audio codecs, PMICs, micro-controller units (MCU) and radio frequency (RF) continue to use Fan-Out Wafer Level Packaging as an alternative to Wafer Level Chip Scale packaging (WLCSP) due to its 5 -sided or 6-sided die protection. As Fan Out packaging becomes mainstream and to get broader adoption of Fan-Out, the need for driving down the cost continues to be at the forefront of Fan-Out suppliers. Traditional 300mm carrier wafer for Fan-Out is currently the mainstream format used for PMICs, RF and other single die applications. As volume of these devices continue to ramp up, the 300mm medium used for Fan-Out processing continues to stay the same. The need for migrating to panel sizes larger than 300mm becomes a necessity to lower down costs and handle higher volumes. Not all products would benefit from migrating from circular 300mm carriers to large panel. If the total area of the panel is not fully utilized, it translates into wasted materials and loss. Currently, there is lack of panel-size standard in the industry. There are various panel sizes such as 300mm x 300mm (square), 457mm x 610mm, 510mm x 515mm, 600mmx 600mm, to name a few. The size of the panels chosen is dependent on utilizing existing equipment or processing capability that would utilize minimal capital expenditure. In 2011, Deca Technologies first established the 600mm panel standard. SEMI’s Fan -Out PLP Panel Task force had formulated a ballot process and 2 panel options had been narrowed down to 510mm x 515mm and 600mm x 600mm (Fan-Out). The 600mm x 600mm format would enable the panel to be singulated into 4x 300mm or 9x200mm square segments to enable utilizing existing probe test equipment in the industry. Coupling the 6-sided die protection process (M-Series) with 600mm x 600mm panel level processing paves the way for innovative methods for Fan-Out processing. New photolithography processing utilizing Adaptive Patterning to auto scale for die shift mitigation, is heavily dependent on segmentation of the panel. In this instance, the 600mm panel is either segmented into 4X 300mm, 9 x 200mm or 1x600mm for Photolithography steps. Depending on the number of fiducials used during the photolithography steps, capital expenditure and exposure accuracy would be highly dependent on the segmentation chosen. In addition, new metrology tools, panel warpage management will need to be considered for quality assurance. This paper will present the background of utilizing 600mm x 600mm panel size, the processing method for 6-sided die protection (M-Series) of a single die, large panel processing considerations, panel level inspection considerations post mold cure, reliability considerations and future of 600mm x 600mm panel level processing for 6-sided die protection.

Artificial neural network and convolutional neural network for prediction of dental caries

Dental caries has high prevalence among kids and adults thus it has become one of the global health concerns. The current modern dentistry focused on the preventives measures to reduce the number of dental caries cases. The employment of machine learning coupled with UV spectroscopy plays a crucial role to detect the early stage of caries. Artificial neural network with hyperparameter tuning was employed to train spectral data for the classification based on the International Caries Detection and Assesment System (ICDAS). Spectra preprocessing namely mean center (MC), autoscale (AS) and Savitzky Golay smoothing (SG) were applied on the data for spectra correction. The best performance of ANN model obtained has accuracy of 0.85 with precision of 1.00. Convolutional neural network (CNN) combined with Savitzky Golay smoothing performed on the spectral data has accuracy, precision, sensitivity and specificity for validation data of 1.00 respectively. The result obtained shows that the application of ANN and CNN capable to produce robust model to be used as an early screening of dental caries.

Classification and determination of sulfur content in crude oil samples by infrared spectrometry

Determining and classifying the sulfur content of crude oil has long been of great importance because of its adverse economic and environmental effects. In this study, the total sulfur concentration in crude oil samples was determined and classified using attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy and chemometric methods. The methods designed for the analysis of crude oils are rapid, economical and non-destructive in the production process of the petroleum industry. Two sets of 70 and 31 samples in regression models were considered for the calibration and prediction sets, respectively. The calibration models were developed using the partial least squares regression model (PLS-R) and support vector machine regression model (SVM-R). Different pre-processing methods were also evaluated for the development of models. The preprocessing methods based on baseline correction, standard normal variate (SNV) and the auto scale were selected for regression and classification models. The use of SVM-R as a non-linear regression provided a model with significantly better root mean square error of prediction (RMSEP) values than the PLS-R model as a linear model. The ATR-FTIR spectral data were also applied by supervised classification method using the partial least squares-discriminant analysis (PLS-DA) and support vector machine-discriminant analysis (SVM-DA) for classifying crude oils based on sulfur content. The samples were classified into two classes according to the sulfur content into sweet and sour crude oil. The result of the classification found an accuracy of 96% and a classification error of 0.0384 for the prediction set in the PLS-DA algorithm. The results indicated that ATR-FTIR spectroscopy associated with multivariate calibration and classification models is a rapid and reliable approach for parallel quantification and qualification of the sulfur content present in crude oils.

Nondestructive Detection of Weight Loss Rate, Surface Color, Vitamin C Content, and Firmness in Mini-Chinese Cabbage with Nanopackaging by Fourier Transform-Near Infrared Spectroscopy.

A nondestructive optical method is described for the quality assessment of mini-Chinese cabbage with nanopackaging during its storage, using Fourier transform-near infrared (FT-NIR) spectroscopy. The sample quality attributes measured included weight loss rate, surface color index, vitamin C content, and firmness. The level of freshness of the mini-Chinese cabbage during storage was divided into three categories. Partial least squares regression (PLSR) and the least squares support vector machine were applied to spectral datasets in order to develop prediction models for each quality attribute. For a comparative analysis of performance, the five preprocessing methods applied were standard normal variable (SNV), first derivative (lst), second derivative (2nd), multiplicative scattering correction (MSC), and auto scale. The SNV-PLSR model exhibited the best prediction performance for weight loss rate (Rp2 = 0.96, RMSEP = 1.432%). The 1st-PLSR model showed the best prediction performance for L* value (Rp2 = 0.89, RMSEP = 3.25 mg/100 g), but also the lowest accuracy for firmness (Rp2 = 0.60, RMSEP = 2.453). The best classification model was able to predict freshness levels with 88.8% accuracy in mini-Chinese cabbage by supported vector classification (SVC). This study illustrates that the spectral profile obtained by FT-NIR spectroscopy could potentially be implemented for integral assessments of the internal and external quality attributes of mini-Chinese cabbage with nanopacking during storage.

A Current Monitor System in High-Voltage Applications in a Range from Picoamps to Microamps

In this article, we present a system to measure current in the range of 0 to 10 μA with high-voltage isolation up to 5 kV. This current monitor consists of three ammeters connected in series, to improve the resolution in the measurement. The design features several innovative elements such as using low voltage to provide power to the devices to measure the current and digitize it with a sampling frequency of 1 KHz, it is generated based on a DC-DC converter that produces three voltages, +12 V, −12 V, and 5 V, from a conventional 10 V source. The three voltages are referenced to the same floating ground. The DC-DC converter has a high voltage insulation up to 5 kV and four optocouplers with an insulation up to 20 kV are used to read the digitized data. The introduction of a DC-DC converter contributed to reduce the noise level in the analog part of the circuit which has been resolved implementing shields inside the board. In particle physics, several systems are used to detect particles in high-energy physics experiments such as Gas Electron Multiplier (GEM), micromegas, etc. GEMs suffer small deteriorations due to discharges in constant operation and require monitoring the current consumption at high frequency (1 kHz). In this work, we present the design and operation of a 0 to 10 μA auto scale ammeter. The results obtained by monitoring the current in a 10 × 10 cm2 GEM are shown.

Validación de la Environmental Reward Observation Scale (EROS) en población Colombiana

Se estimaron las propiedades psicométricas de la Environmental Reward Observation Scale (EROS) en población colombiana, con dos muestras: población comunitaria y clínica. Para la primera se evaluaron 507 personas en 22 departamentos del país con edades entre los 18 y los 72 años, 60,5% son mujeres y restante hombres, la segunda muestra estuvo compuesta por 68 personas con edades comprendidas entre los 18 y 72 años que se atendidas en el momento de la evaluación por profesionales en salud mental. Se realizó la aplicación de los instrumentos (Enviromental Reward Observation Scale Y Escala Auto Aplicada De Depresión De Zung) por medio de redes sociales y correos electrónicos, con el fin de tener una muestra más amplia y una mayor variabilidad de la misma. Se encontraron apropiados niveles de consistencia interna (alfa de 0,87) e importantes evidencias de validez de constructo (agrupación en un factor que explica el 46,82% de la varianza total) y discriminante (r=-0,67).

EON OF IMPLEMENTING A MULTIFACETED CLOUD BASED OCR IN APPLE’S COMPASSIONATE APP STORE MILIEU

Cloud Architectures discourse key hitches surrounding large-scale data dispensation. In customary data processing it is grim to get as many machines as an application needs. Second, it is difficult to get the machines when one needs them. Third, it is difficult to dispense and harmonize a large-scale job on different machines, run processes on them, and provision another machine to recover if one machine fails. Fourth, it is difficult to auto scale up and down based on dynamic workloads. Fifth, it is difficult to get rid of all those machines when the job is done. Cloud Architectures solve such difficulties.Optical character recognition of cursive scripts present a number of thought-provokingsnags in both segmentation and recognition processes and this entices many researches in the arena of contraption learning. This paper presents the best approach based on a mishmash of OCR and Cloud Computing to handle with the Apple’s prerequisite, to make it available in the app store to design a splendid OCR for outdoor portable documents. The enactment results on a comprehensive database show a high notch of accuracy which meets the requirements of viable use.

Determination of Total Volatile Basic Nitrogen (TVB-N) Content in Beef by Hyperspectral Imaging Technique

Non-destructive determination of TVB-N content in beef using hyperspectral imaging (HSI) technique was evaluated. In order to create a robust model to predict the TVB-N content in beef, partition of sample set, spectral pretreatment, and the optimum wavelength selection were discussed. After the beef sample set was parted by concentration gradient (CG) algortithm, and the spectra of beef samples were preprocessed by standard normalized variate (SNV) combined with auto scale(AS), the partial least square regression (PLSR) model was established using the full spectral range, which had the best prediction abilities with R cv 2 of 0.9124, R p 2 of 0.8816, RMSECV of 1.5889, and RMSEP of 1.7719, respectively. After the optimum wavelengths which is closely related to the TVB-N content of beef samples was obtained using the competitive adaptive re-weighted (CARS) algorithm, a new PLSR model was established using the optimum wavelengths, which had outstanding prediction abilities with R cv 2 of 0.9235, R p 2 of 0.9241, RMSECV of 1.4881, and RMSEP of 1.4882, respectively.The study showed that HSI is a powerful technique to predict the TVB-N content in beef by a nondestructive way.

FT-Raman Spectroscopy and PLS Regression for Quantitative Determination of Captopril in Tablet Powder; Effect of Different Preprocessing Methods

Aims: This study was performed to develop a fast and simple method for the quantitative analysis of Captopril in tablet powder by FT-Raman Spectroscopy and PLS regression. The effect of different preprocessing methods on multivariate calibration was studied. Methodology: Four different preprocessing methods, namely mean center (MC), autoscale (AS), normalization (N1) and orthogonal signal correction (OSC) were applied to reduce the impact of sources of variability, such as instrument drift and sampling variability, correct for path length variation and increase the signal to noise ratio. PLS regression models were fitted and their predictive performance was evaluated by the root mean square error of prediction (RMSEP) and number of latent variables (LVs). Research Article American Chemical Science Journal, 4(1): 24-37, 2014 25 Place and Duration of Study: School of Pharmacy, University of Maryland, between July to November 2012. Results: N1-AS combination (in this order) was found to give best results as it decreased the RMSEP by 15 % (from 0.92 to 0.77) with same number of latent variable used in the model (2 LVs). Other preprocessing methods have not yielded good results for this application. The results obtained by the proposed method were compared with those of the reported HPLC method. No significant difference has been observed regarding both accuracy and precision. Conclusion: FT-Raman Spectroscopy with PLS regression can be used as a reliable alternative for HPLC method, as it is faster and does not require sample pretreatment procedures.

Investigation of metabonomics technique by analyze of NMR data, which method is better? Mean center or auto scale?

The factors such as disease can disrupt homeostasis, resulting in perturbations of endogenous biochemicals that are involved in key metabolic profiles. Metabonomics is useful technique to quantitative description of endogenous metabolites present in a biological sample such as urine, plasma and tissue. High resolution 1 H nuclear magnetic resonance (NMR)-based metabonomics is a technique used to analyze and interpret multivariate metabolic data that correlate with changes of physiological conditions. Before any explanation for metabolite data, preprocessing the spectroscopic data is essential. In this paper, we show scaling effects in metabonomics investigation of patients diagnosed with Crohn's and Celiac disease. two techniques of scaling were applied as follows: mean centering and auto scaling. Results reveal that the mean centering is more useful to segregate patients from healthy subjects in the data set of Crohn's and Celiac disease.

Study on Large Scale Analysis for Infra-Structure

During the last two decades, the interests of large scale seamless analysis have been increased with respect to lifetime extension and management of infra-structure. And then, a seamless analysis of complex geometry is one of greatly interesting topic. However, there are still gaps between the industrial applications and fundamental academic studies owing to time consuming the modeling and analyzing process. To resolve these problems, the integrity evaluation procedure from knowledge-based information system (KIS) to ADVENTRUE has been proposed for the application of auto mesh generation and large scale analysis. The virtual plant which is used for the master of KIS, provides the 3D information of entities for mesh generation. And then, the ADVENTURE makes a domain decomposed mesh and analyzes it using hierarchical domain decomposition method. Considering the good applicability in both efficiency and exactness point of views, it is anticipated that the proposed study on large scale analysis can be used as a promising integrity evaluation method of infra-structures.