System manufacturing test cost model

Williams, David; Ambler, A.P.

doi:10.1109/test.2002.1041798

Cited by 14 publications

(5 citation statements)

References 3 publications

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“…[8] gives a framework for analyzing the impact of BIST and boundary scan at the board and field levels. [11] describes a model for an assembly factory, and [5] discusses system test costs, but without considering the impact of DFT.…”

Section: Previous Workmentioning

confidence: 99%

“…One of the reasons for this is the lack of a full stream cost and benefit model, stretching from fab to field, that can help DFT proponents justify the addition of testability. There have been numerous models proposed at the IC level ( Wei [10], for instance) and models proposed at the system level, [11] but none with details on the costs and benefits at each stage of system design and integration, and none focused on the justification of DFT.…”

Section: Introductionmentioning

confidence: 99%

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Justifying DFT with a Hierarchical Top-Down Cost-Benefit Model

Davidson

2008

2008 IEEE International Test Conference

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How can we justify system level DFT? We must show that it has a positive return on investment (ROI). Existing test ROI models are manufacturing centric, do not account for the disaggregation of the product realization process, and are often focused on a specific DFT method. We propose a new, top-down, hierarchical ROI model, which starts with potential benefits and can handle entire systems more effectively than current models. IntroductionIt is well known that Design for Testability (DFT) improves quality and saves money. While DFT methods are now standard for most ICs, DFT at the system level is more problematic. One of the reasons for this is the lack of a full stream cost and benefit model, stretching from fab to field, that can help DFT proponents justify the addition of testability. There have been numerous models proposed at the IC level ( Wei [10], for instance) and models proposed at the system level, [11] but none with details on the costs and benefits at each stage of system design and integration, and none focused on the justification of DFT.The purpose of this paper is to provide such a model, but we will go about this in a slightly different way from previous efforts. These have labored to be precise and accurate. To do this, previous models are overly complex, and depend on parameters not known yet or difficult to obtain. Our model, on the other hand, has two goals:• Be detailed enough to give the DFT engineer advice on the important factors to be decided when making the decision to request DFT, and how much to propose. • Provide a convincing financial argument for the benefits of DFT. We don't believe that a manager deadset against DFT will be convinced by any model, but one in favor of it or leaning towards it will value a model to help convince upper management of the value of this effort. This means that the model must be kept simple and parameters which don't significantly affect the use/no-use decision should be dropped. There are some things considered in our model which we think have not been handled well in previous work:• Uncertainty. While Wei's model considers the uncertainty of both costs and benefits, this must be a fundamental part of any model. Understanding the benefits of DFT for an existing product and manufacturing process introduces far less uncertainty than trying to do so for a product yet to be built or designed.• The impact of disaggregation. DFT was first used by vertically integrated companies, where some additional expense at the IC level could benefit the companies bottom line at the system level. Today more systems are built from standard parts, and manufacturing has been outsourced. This limits the freedom of the DFT proponent to add DFT. • The impact of the distribution of actual field failures on the benefit analysis. While it is tough to make predictions, especially about the future, as Yogi Berra said, we want DFT to address the failures most likely to happen. Our model must take all of these factors into account. More importantly, the DFT advocate must take these...

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Section: Previous Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Justifying DFT with a Hierarchical Top-Down Cost-Benefit Model

Davidson

2008

2008 IEEE International Test Conference

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“…(4) C re ' : the cost of repair service except repair test time C lcc (t 2 ) : the cost of life-cycle C rtt (t 3 ) : the cost of repair and test time t 3 : the repair and test time…”

Section: Field Service Modelmentioning

confidence: 99%

“…C ost : the overall system-level test cost t 1 : the test time in manufacturing system t 2 : the customer run time t 3 : the repair and test time C omt :the cost of the manufacturing system test (t 1 +t 2 ): the failure occurrence rate C fs : the overall cost of field service A lot of effort has been made to reduce the cost of the overall manufacturing system test, C omt . Farren and Ambler [6] showed that this cost can be reduced by optimizing the test time, t 1 , in the manufacturing system.…”

Section: Overall System-level Test Modelmentioning

confidence: 99%

The economics of field service for electronic systems

Lin

Lee

Ambler

IEEE Autotestcon, 2005.

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Field service is a complex process that involves in sending technicians to perform repair tasks on consumer products. It is responsible for having failure products fixed and keeping a low service cost. The cost of field service is an important corporate activity but it is often not well understood. Minimal cost of field service is particularly critical to global competitiveness for an electronic company. The economic analysis of field service is a useful analytical tool to yield a low field service cost and achieve high customer satisfactory. This paper presents a field service cost model and explores all aspects of overall system-level test for electronic systems.

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“…A study of whether DFT techniques are required for a chip is given in [8], [9]. But addressing the issue related to the need for fault tolerance for a chip involves certain parameters which are not discussed in those models.…”

Section: Introductionmentioning

confidence: 99%