Decision-Support Models for Thermo-Mechanical Reliability of Leadfree Flip-Chip Electronics in Extreme Environments

2005 ◽  
Author(s):  
P. Lall ◽  
N. Singh ◽  
M. Strickland ◽  
J. Blanche ◽  
J. Suhling
Keyword(s):  
Decision Support ◽  
Flip Chip ◽  
Extreme Environments ◽  
Mechanical Reliability ◽  
Decision Support Models

Models of decision support in socio-cyber-physical systems

2019 ◽  
pp. 55-70 ◽  
Author(s):  
A. V. Smirnov ◽  
T. V. Levashova
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Situation Awareness ◽  
Cyber Physical Systems ◽  
Emergent Behavior ◽  
Emergent Properties ◽  
Decision Making Process ◽  
Physical Systems ◽  
Decision Support Models ◽  
Common Goals

Introduction: Socio-cyber-physical systems are complex non-linear systems. Such systems display emergent properties. Involvement of humans, as a part of these systems, in the decision-making process contributes to overcoming the consequences of the emergent system behavior, since people can use their experience and intuition, not just the programmed rules and procedures.Purpose: Development of models for decision support in socio-cyber-physical systems.Results: A scheme of decision making in socio-cyber-physical systems, a conceptual framework of decision support in these systems, and stepwise decision support models have been developed. The decision-making scheme is that cybernetic components make their decisions first, and if they cannot do this, they ask humans for help. The stepwise models support the decisions made by components of socio-cyber-physical systems at the conventional stages of the decision-making process: situation awareness, problem identification, development of alternatives, choice of a preferred alternative, and decision implementation. The application of the developed models is illustrated through a scenario for planning the execution of a common task for robots.Practical relevance: The developed models enable you to design plans on solving tasks common for system components or on achievement of common goals, and to implement these plans. The models contribute to overcoming the consequences of the emergent behavior of socio-cyber-physical systems, and to the research on machine learning and mobile robot control.

Interfacial Compounds Characteristic and Its Reliability Effects on SAC305 Microjoints in Flip Chip Assemblies

2018 ◽  
Vol 140 (3) ◽  
Author(s):  
Ye Tian ◽  
Ning Ren ◽  
Xiaoxia Jian ◽  
Tie Geng ◽  
Yiping Wu
Keyword(s):  
Site Effects ◽  
Flip Chip ◽  
Fe Simulation ◽  
Great Influence ◽  
Underlying Mechanism ◽  
Edge Region ◽  
Mechanical Reliability ◽  
Reaction Products ◽  
Interfacial Imcs ◽  
Center Region

This study mainly focuses on site effects of the Ni pad interface on intermetallic compounds (IMCs) characteristic during assembly reflowing, and attempts to provide a reasonable explanation for this particular finding. Besides, the changes of the resulting IMCs characteristic are characterized during thermal shock (TS) cycling, and their potential influences on thermal–mechanical reliability of microjoints are evaluated experimentally and numerically. The results show that the site on the Ni pad interface of silicon chip has great influence on interfacial reaction products, i.e., interfacial IMCs. After bumps soldering, a great amount of larger diamond-shaped (Cu, Ni)6Sn5 compounds were densely packed at the edge region, while some smaller ones were only scattered at the center region. Moreover, substantial particle-shaped (Ni, Cu)3Sn4 compounds as well as some rod-shaped ones emerged at the spaces between the (Cu, Ni)6Sn5 compounds of the center region. More importantly, such site effects were remained in the microjoints during TS cycling, which induced the formation of larger protruding (Cu, Ni)6Sn5 compounds. Finite element (FE) simulation results showed that the stress was mainly concentrated at the top of the protruding (Cu, Ni)6Sn5 compounds, which can be a critical reason to cause the crack occurrence. Furthermore, the underlying mechanism of the interfacial IMCs characteristic induced by the site effects was attempted to propose during bumps soldering.

Efficiency Analysis of Turkey's Transportation System Using Decision Support Model

2016 ◽  
pp. 61-92
Author(s):  
Adnan Sozen ◽  
Fatih Cipil
Keyword(s):  
European Union ◽  
Decision Support ◽  
Malmquist Index ◽  
Efficiency Analysis ◽  
The European Union ◽  
Railway Transportation ◽  
Decision Support Model ◽  
Decision Support Models ◽  
Policy Objectives ◽  
The Eu

Since the European Union has certain targets and criteria that must be fulfilled by its members, Turkey's situation in transportation compared to the EU member countries (23 countries whose data were accessed) was examined through the approaches of decision support models (Data Envelopment Analysis and Malmquist Index). This study investigates whether Turkey utilizes its road, airway and railway transportation indicators efficiently or not within the framework of the European Union (EU) accession process. In addition, it aims to demonstrate Turkey's current position compared to other EU countries by performing relative efficiency analysis on road, airway and railway transportation indicators. The conclusions of this study will help policymakers to determine Turkey's policy objectives for its integration to the EU in terms of transportation indicators.

Some Selected Case Study Applications

2016 ◽  
pp. 180-270
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Decision Process ◽  
Support Systems ◽  
Labor Cost ◽  
The Matrix ◽  
Markov Decision ◽  
Decision Support Models ◽  
Minimization Technique

Having examined the modelling principles of underpinning based decision support systems applied to construction in Chapter 6, this chapter will now demonstrate their detail applications in construction practice. Specifically, 7 decision-support systems will be examined. The choices are based on the fact that data for use in the decision support models are available. The decision-support systems considered are the matrix-based used in determining labor cost, sub-chaining method, linear regression, optimization (i.e. minimization) technique, Markov decision process and rule-based systems.

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