Transistor-Level Gate Modeling for Nano CMOS Circuit Verification Considering Statistical Process Variations

2011 ◽  
pp. 190-199
Author(s):  
Qin Tang ◽  
Amir Zjajo ◽  
Michel Berkelaar ◽  
Nick van der Meijs
Keyword(s):  
Process Variations ◽  
Cmos Circuit ◽  
Statistical Process ◽  
Circuit Verification

CMOS Logic Gates Leakage Modeling Under Statistical Process Variations

2010 ◽  
pp. 191-202
Author(s):  
Carmelo D’Agostino ◽  
Philippe Flatresse ◽  
Edith Beigne ◽  
Marc Belleville
Keyword(s):  
Logic Gates ◽  
Process Variations ◽  
Statistical Process

scholarly journals An EWMA-DiD Control Chart to Capture Small Shifts in the Process Average Using Auxiliary Information

Technologies ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 69
Author(s):  
Muhammad Mughal ◽  
Muhammad Azam ◽  
Muhammad Aslam
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Average Run Length ◽  
Auxiliary Information ◽  
Process Variations ◽  
Specific Situation ◽  
Practical Application ◽  
Statistical Process ◽  
Real Scenario ◽  
Better Than

Among the Statistical Process Control (SPC) techniques, control charts are considered to be high weight-age due to their effectiveness in process variation. As the Shewhart’s charts are not that active in monitoring small and moderate process variations, the statisticians have been making efforts to improve the performance of the control chart by introducing several techniques within the tool. These techniques consist of experimenting with different estimators, different sampling selection techniques, and mixed methodologies. The proposed chart is one of the examples of a mixed chart technique that has shown its efficiency in monitoring small variations better than any of the existing techniques in the specific situation of auxiliary information. To show and compare its performance, average run length (ARL) tables and ARL curves have been presented in the article. An industrial example has also been included to show the practical application of the proposed chart in a real scenario.

Contributions Regarding the Improvement of Turning Processes Using Statistical Process Control Method

2016 ◽  
Vol 859 ◽  
pp. 188-193
Author(s):  
Emilia Maria Campean ◽  
Călin Ciprian Otel ◽  
Emanuela Pop
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Quality Management System ◽  
Control Method ◽  
Negative Impact ◽  
Effective Means ◽  
Process Variations ◽  
Depth Of Cut ◽  
Statistical Process ◽  
Regime Changes

This paper aims documentary research of theoretical and experimental statistical process control in industrial engineering. The most effective means to reduce process variations against the customer requirements is to implement a quality management system based on statistical process control tools. Cutting regime changes in turning processes are based on careful monitoring of vibration. As soon as it is found in the milling excessive noise or vibration the measure to which it resort is to reduce one or more parameters (speed, feed and depth of cut) to remove the effect. Finally, the measure has a negative impact on performance.

Real-time automatic statistical control for the grinding process

1997 ◽  
Vol 211 (3) ◽  
pp. 233-237 ◽  
Author(s):  
J P T Mo ◽  
M R Squires ◽  
G C Brien
Keyword(s):  
Real Time ◽  
Process Variations ◽  
Statistical Control ◽  
Level Control ◽  
Statistical Process ◽  
Temperature Changes ◽  
Cnc Grinding ◽  
High Level ◽  
Grinding Operations ◽  
Control Methodology

The highly consistent axes control of current computer numerically controlled (CNC) grinding machines has ensured a high-quality output provided that the process parameters can be kept constant. However, there are many variations in the process that give rise to differences between workpieces. Typical process variations such as tool wear, materials and temperature changes are inevitable and cannot be compensated by advancements in axis servo-control. This paper addresses this problem by applying a high-level control methodology which makes use of real-time statistical process control (SPC) information to determine whether the process has deteriorated due to process variations. Adjustments to machine parameters to offset these changes can then be made. The methodology has been successfully incorporated in a control loop within the controller of the CNC grinders to achieve truly unattended grinding operations.

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