Using simulation to determine the batch size for I/O drawer test process in a high-end server manufacturing environment

2014 ◽  
Author(s):  
Lawrence Al-Fandi ◽  
Faisal Aqlan
Keyword(s):  
Batch Size ◽  
Manufacturing Environment ◽  
Server Manufacturing ◽  
Test Process

scholarly journals Using simulation with Design For Six Sigma in a server manufacturing environment

2008 ◽  
Author(s):  
Sreekanth Ramakrishnan ◽  
Pei-Fang Tsai ◽  
Christiana M. Drayer ◽  
Krishnaswami Srihari
Keyword(s):  
Six Sigma ◽  
Manufacturing Environment ◽  
Design For Six Sigma ◽  
Server Manufacturing

Scanning-probe microscope analysis of optical thin films: A new analytical tool in a manufacturing environment

1994 ◽  
Vol 52 ◽  
pp. 1068-1069
Author(s):  
S. P. Sapers ◽  
R. Clark ◽  
P. Somerville
Keyword(s):  
Thin Film ◽  
Thin Films ◽  
Thermal Control ◽  
Scanning Probe Microscope ◽  
Scanning Probe ◽  
List Type ◽  
Manufacturing Environment ◽  
Physical Measurements ◽  
Probe Microscope

OCLI is a leading manufacturer of thin films for optical and thermal control applications. The determination of thin film and substrate topography can be a powerful way to obtain information for deposition process design and control, and about the final thin film device properties. At OCLI we use a scanning probe microscope (SPM) in the analytical lab to obtain qualitative and quantitative data about thin film and substrate surfaces for applications in production and research and development. This manufacturing environment requires a rapid response, and a large degree of flexibility, which poses special challenges for this emerging technology. The types of information the SPM provides can be broken into three categories:(1)Imaging of surface topography for visualization purposes, especially for samples that are not SEM compatible due to size or material constraints;(2)Examination of sample surface features to make physical measurements such as surface roughness, lateral feature spacing, grain size, and surface area;(3)Determination of physical properties such as surface compliance, i.e. “hardness”, surface frictional forces, surface electrical properties.

Analysis of Leakage Failures in Flash Memory Devices and Root Cause Identification

2000 ◽  
Author(s):  
J. N. C. de Luna ◽  
M. O. del Fierro ◽  
J. L. Muñoz
Keyword(s):  
Flash Memory ◽  
Electromagnetic Interference ◽  
Yield Loss ◽  
High Amplitude ◽  
Physical Analysis ◽  
Input Buffer ◽  
Automated Test Equipment ◽  
Root Cause ◽  
Root Cause Identification ◽  
Test Process

Abstract An advanced flash bootblock device was exceeding current leakage specifications on certain pins. Physical analysis showed pinholes on the gate oxide of the n-channel transistor at the input buffer circuit of the affected pins. The fallout contributed ~1% to factory yield loss and was suspected to be caused by electrostatic discharge or ESD somewhere in the assembly and test process. Root cause investigation narrowed down the source to a charged core picker inside the automated test equipment handlers. By using an electromagnetic interference (EMI) locator, we were able to observe in real-time the high amplitude electromagnetic pulse created by this ESD event. Installing air ionizers inside the testers solved the problem.

A Turbo-Team Approach to Establishing a Software Test Process at Union Switch and Signal.

1997 ◽  
Author(s):  
Don McAndrews ◽  
Janice M. Ryan ◽  
Priscilla Fowler
Keyword(s):  
Software Test ◽  
Team Approach ◽  
Test Process

3 (2) Factorial Design Assisted Crushed Puffed Rice-HPMC-Chitosan based Hydrodynamically Balanced System of Metoprolol Succinate

2020 ◽  
Vol 10 (3) ◽  
pp. 237-249
Author(s):  
Shashank Soni ◽  
Veerma Ram ◽  
Anurag Verma
Keyword(s):  
Drug Release ◽  
Factorial Design ◽  
Hydroxypropyl Methylcellulose ◽  
Batch Size ◽  
Metoprolol Succinate ◽  
Zero Order Kinetics ◽  
Two Factors ◽  
Puffed Rice ◽  
Model Drug

Introduction: Hydrodynamically balanced system (HBS) possesses prolonged and continuous delivery of the drug to the gastrointestinal tract which improves the rate and extent of medications that have a narrow absorption window. The objective of this work was to develop a Hydrodynamically Balanced System (HBS) of Metoprolol Succinate (MS) as a model drug for sustained stomach specific delivery. Materials and Methods: Experimental batches were designed according to 3(2) Taguchi factorial design. A total of 9 batches were prepared for batch size 100 capsules each. Formulations were prepared by physically blending MS with polymers followed by encapsulation into hard gelatin capsule shell of size 0. Polymers used were Low Molecular Weight Chitosan (LMWCH), Crushed Puffed Rice (CPR), and Hydroxypropyl Methylcellulose K15 M (HPMC K15M). Two factors used were buoyancy time (Y1) and time taken for 60% drug release (T60%; Y2). Results: The drug excipient interaction studies were performed by the thermal analysis method which depicts that no drug excipient interaction occurs. In vitro buoyancy studies and drug release studies revealed the efficacy of HBS to remain gastro retentive for a prolonged period and concurrently sustained the release of MS in highly acidic medium. All formulations followed zero-order kinetics. Conclusion: Developed HBS of MS with hydrogel-forming polymers could be an ideal delivery system for sustained stomach specific delivery and would be useful for the cardiac patients where the prolonged therapeutic action is required.

State-dependent signalling in queueing networks

1994 ◽  
Vol 26 (02) ◽  
pp. 436-455 ◽  
Author(s):  
W. Henderson ◽  
B. S. Northcote ◽  
P. G. Taylor
Keyword(s):  
Queueing Networks ◽  
State Dependence ◽  
Product Form ◽  
Batch Size ◽  
Single Server ◽  
Negative Customers ◽  
Affect Control ◽  
State Dependent ◽  
Special Cases ◽  
Equilibrium Distributions

It has recently been shown that networks of queues with state-dependent movement of negative customers, and with state-independent triggering of customer movement have product-form equilibrium distributions. Triggers and negative customers are entities which, when arriving to a queue, force a single customer to be routed through the network or leave the network respectively. They are ‘signals' which affect/control network behaviour. The provision of state-dependent intensities introduces queues other than single-server queues into the network. This paper considers networks with state-dependent intensities in which signals can be either a trigger or a batch of negative customers (the batch size being determined by an arbitrary probability distribution). It is shown that such networks still have a product-form equilibrium distribution. Natural methods for state space truncation and for the inclusion of multiple customer types in the network can be viewed as special cases of this state dependence. A further generalisation allows for the possibility of signals building up at nodes.

scholarly journals Millimeter-Wave Radar Sensor for Automated Tomographic Imaging of Composite Materials in a Manufacturing Environment

2021 ◽  
Vol 5 (3) ◽  
pp. 1-4
Author(s):  
Dominik Meier ◽  
Christian Zech ◽  
Benjamin Baumann ◽  
Bersant Gashi ◽  
Matthias Malzacher ◽  
...  
Keyword(s):  
Composite Materials ◽  
Millimeter Wave ◽  
Tomographic Imaging ◽  
Radar Sensor ◽  
Manufacturing Environment ◽  
Millimeter Wave Radar ◽  
Wave Radar

scholarly journals Real UAV-Bird Image Classification Using CNN with a Synthetic Dataset

2021 ◽  
Vol 11 (9) ◽  
pp. 3863
Author(s):  
Ali Emre Öztürk ◽  
Ergun Erçelebi
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Corner Detection ◽  
Batch Size ◽  
Test Accuracy ◽  
Classification Problems ◽  
Auc Value ◽  
Classification Test

A large amount of training image data is required for solving image classification problems using deep learning (DL) networks. In this study, we aimed to train DL networks with synthetic images generated by using a game engine and determine the effects of the networks on performance when solving real-image classification problems. The study presents the results of using corner detection and nearest three-point selection (CDNTS) layers to classify bird and rotary-wing unmanned aerial vehicle (RW-UAV) images, provides a comprehensive comparison of two different experimental setups, and emphasizes the significant improvements in the performance in deep learning-based networks due to the inclusion of a CDNTS layer. Experiment 1 corresponds to training the commonly used deep learning-based networks with synthetic data and an image classification test on real data. Experiment 2 corresponds to training the CDNTS layer and commonly used deep learning-based networks with synthetic data and an image classification test on real data. In experiment 1, the best area under the curve (AUC) value for the image classification test accuracy was measured as 72%. In experiment 2, using the CDNTS layer, the AUC value for the image classification test accuracy was measured as 88.9%. A total of 432 different combinations of trainings were investigated in the experimental setups. The experiments were trained with various DL networks using four different optimizers by considering all combinations of batch size, learning rate, and dropout hyperparameters. The test accuracy AUC values for networks in experiment 1 ranged from 55% to 74%, whereas the test accuracy AUC values in experiment 2 networks with a CDNTS layer ranged from 76% to 89.9%. It was observed that the CDNTS layer has considerable effects on the image classification accuracy performance of deep learning-based networks. AUC, F-score, and test accuracy measures were used to validate the success of the networks.

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