scholarly journals Failure Test Optimization Method of Silicon Pressure Sensor Based on Zero Failure Data

2019 ◽  
Vol 626 ◽  
pp. 012007
Author(s):  
Lei Zuo ◽  
Hongkai Sun
Keyword(s):  
Pressure Sensor ◽  
Optimization Method ◽  
Failure Data ◽  
Test Optimization

3D SoC test optimization method based on balance of scan chain

2013 ◽  
Vol 26 (7) ◽  
pp. 586-590
Author(s):  
Wei Wang ◽  
Xin Li ◽  
Tian Chen ◽  
Jun Liu ◽  
Fang Fang ◽  
...  
Keyword(s):  
Optimization Method ◽  
Soc Test ◽  
Scan Chain ◽  
Test Optimization

scholarly journals Energy Consumption-Based Maintenance Policy Optimization

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5674
Author(s):  
Ágota Bányai
Keyword(s):  
Energy Consumption ◽  
Manufacturing Systems ◽  
Discrete Event ◽  
Functional Model ◽  
Optimization Method ◽  
Maintenance Policy ◽  
Failure Data ◽  
Maintenance Policies ◽  
Value Determination ◽  
Policy Optimization

The optimal predictive, preventive, corrective and opportunistic maintenance policies play an important role in the success of sustainable maintenance operations. This study discusses a new energy efficiency-related maintenance policy optimization method, which is based on failure data and status information from both the physical system and the digital twin-based discrete event simulation. The study presents the functional model, the mathematical model and the solution algorithm. The maintenance optimization method proposed in this paper is made up of four main phases: computation of energy consumption based on the levelized cost of energy, computation of GHG emission, computation of value determination equations and application of the Howard’s policy iteration techniques. The approach was tested with a scenario analysis, where different electricity generation sources were taken into consideration. The computational results validated the optimization method and show that optimized maintenance policies can lead to an average of 38% cost reduction regarding energy consumption related costs. Practical implications of the proposed model and method regard the possibility of finding optimal maintenance policies that can affect the energy consumption and emissions from the operation and maintenance of manufacturing systems.

Orbital Experience from an Integration and Test Perspective

1998 ◽  
Vol 41 (6) ◽  
pp. 34-41
Author(s):  
William Tosney ◽  
Andrew Quintero
Keyword(s):  
Large Population ◽  
Space Vehicle ◽  
Physics Of Failure ◽  
Failure Data ◽  
Test Strategies ◽  
Mission Success ◽  
Reduction Strategies ◽  
Environmental Test ◽  
Test Optimization ◽  
The Impact

Space vehicle schedule and cost reduction strategies tend to focus on test optimization without a comprehensive analytical approach to the impact on mission success. This study provides valuable stochastic insights into orbital physics of failure while identifying potential relationships to ground integration and test processes by analyzing data across a large population of space vehicle programs. Failure data analysis uses reliability growth modeling techniques to provide greater insight into environmental test effectiveness. Results show correlations between orbital failures, environmental test, hardware retest, and hardware categories. Test strategies are discussed to mitigate risk of orbital failures for hardware subjected to varying degrees of retest and late integration.

Comprehensive Optimization Based on Entropy Weight of Electromechanical Product Reliability Evaluation Model

2014 ◽  
Vol 668-669 ◽  
pp. 1611-1614
Author(s):  
Yan Ping Cun ◽  
Yun Teng ◽  
Hong Liang Sun
Keyword(s):  
Evaluation Model ◽  
Optimization Method ◽  
Reliability Evaluation ◽  
Empirical Models ◽  
Product Reliability ◽  
Entropy Weight ◽  
Evaluation Indexes ◽  
Failure Data ◽  
Method Of Evaluation ◽  
Electromechanical Product

The failure data of the electromechanical product satisfies a certain law that a batch of failure data usually meet a variety of distributions at the same time. As to this phenomenon, an optimization method of evaluation model of reliability which is combined with the concept of entropy was put forward in this work. In the optimization process, several empirical models and multiple evaluation indexes were proposed for selecting a best model of reliability evaluation model, and entropy weight was defined to show the importance of evaluation indexes.

Design optimization of a high-sensitive absolute micro-pressure sensor

Sensor Review ◽  
2014 ◽  
Vol 34 (3) ◽  
pp. 312-318 ◽  
Author(s):  
Zhongliang Yu ◽  
Yulong Zhao ◽  
Lili Li ◽  
Cun Li ◽  
Xiawei Meng ◽  
...  
Keyword(s):  
Pressure Sensor ◽  
Optimization Problems ◽  
High Sensitivity ◽  
Optimization Method ◽  
Structure Design ◽  
Content Type ◽  
Design And Optimization ◽  
Nonlinear Fitting ◽  
Silicon Bulk ◽  
Dynamic Performances

Purpose – The purpose of this study is to develop a piezoresistive absolute micro-pressure sensor for altimetry. For this application, both high sensitivity and high overload resistance are required. To develop a piezoresistive absolute micro-pressure sensor for altimetry, both high sensitivity and high-overload resistance are required. The structure design and optimization are critical for achieving the purpose. Besides, the study of dynamic performances is important for providing a solution to improve the accuracy under vibration environments. Design/methodology/approach – An improved structure is studied through incorporating sensitive beams into the twin-island-diaphragm structure. Equations about surface stress and deflection of the sensor are established by multivariate fittings based on the ANSYS simulation results. Structure dimensions are determined by MATLAB optimization. The silicon bulk micromachining technology is utilized to fabricate the sensor prototype. The performances under both static and dynamic conditions are tested. Findings – Compared with flat diaphragm and twin-island-diaphragm structures, the sensor features a relatively high sensitivity with the capacity of suffering atmosphere due to the introduction of sensitive beams and the optimization method used. Originality/value – An improved sensor prototype is raised and optimized for achieving the high sensitivity and the capacity of suffering atmosphere simultaneously. A general optimization method is proposed based on the multivariate fitting results. To simplify the calculation, a method to linearize the nonlinear fitting and optimization problems is presented. Moreover, a differential readout scheme attempting to decrease the dynamic interference is designed.

scholarly journals Genetic Algorithm-Based Digital Test Optimization Method and its Application to Yield Improvement

2021 ◽  
Author(s):  
Y.H. Chan ◽  
S.H. Goh
Keyword(s):  
Genetic Algorithm ◽  
Optimization Methods ◽  
Optimization Method ◽  
Turnaround Time ◽  
Third Party ◽  
Product Launch ◽  
Technology Scaling ◽  
Product Test ◽  
Genetic Algorithm Approach ◽  
Test Optimization

Abstract Narrowing design and manufacturing process margins with technology scaling are one of the causes for a reduction in IC chip test margin. This situation is further aggravated by the extensive use of third-party design blocks in contemporary system-on-chips which complicates chip timing constraint. Since a thorough timing verification prior to silicon fabrication is usually not done due to aggressive product launch schedules and escalating design cost, occasionally, a post-silicon timing optimization process is required to eliminate false fails encountered on ATE. An iterative two-dimensional shmoo plots and pin margin analysis are custom optimization methods to accomplish this. However, these methods neglect the interdependencies between different IO timing edges such that a truly optimized condition cannot be attained. In this paper, we present a robust and automated solution based on a genetic algorithm approach. Elimination of shmoo holes and widening of test margins (up to 2x enhancements) are demonstrated on actual product test cases. Besides test margin optimization, this method also offers insights into the criticality of test pins to accelerate failure debug turnaround time.

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