Reliability prediction for electronic components: Difference between revisions

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A [[prediction]] of reliability is an important element in the process of selecting equipment for use by [[telecommunications]] [[service providers]] and other buyers of [[electronic equipment]], and it is essential during the design stage of engineering systems life cycle.<ref>EPSMA, “Guidelines to Understanding Reliability Predictions”, EPSMA, 2005</ref> Reliability is a measure of the [[frequency]] of equipment failures as a function of time. [[wikt:reliability|Reliability]] has a major impact on maintenance and repair costs and on the continuity of service.<ref>Terry Donovan, Senior Systems Engineer Telcordia Technologies. Member of Optical Society of America, IEEE, "Automated Reliability Prediction, SR-332, Issue 3", January 2011; "Automated Reliability Prediction (ARPP), FD-ARPP-01, Issue 11", January 2011</ref>

Every product has a [[failure rate]], λ which is the number of units failing per unit time. This failure rate changes throughout the life of the product. It is the [[manufacturer]]’s's aim to ensure that product in the “infant mortality period” does not get to the [[customer]]. This leaves a product with a useful life period during which failures occur randomly i.e., λ is constant, and finally a wear-out period, usually beyond the productsproduct's useful life, where λ is increasing.

== Definition of reliability ==

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Reliability predictions:

:* '''Help assess the effect of product reliability on the maintenance activity and on the quantity of spare units required for acceptable field performance of any particular system.''' For example, predictions of the frequency of unit level maintenance actions can be obtained. Reliability prediction can be used to size spare populations.

:* '''Provide necessary input to system-level reliability models.''' System-level reliability models can subsequently be used to predict, for example, frequency of system outages in [[Steady state (electronics)|steady-state]], frequency of system outages during early life, expected [[downtime]] per year, and system availability. <ref>{{cite web| url=https://www.tronicszone.com/ | title= Reliability Prediction| accessdate = 13 July 2015}}</ref>

:* '''Provide necessary input to unit and system-level life cycle cost analyses.''' [[Whole-life cost|Life cycle cost studies]] determine the cost of a product over its entire life. Therefore, how often a unit will have to be replaced needs to be known. Inputs to this process include unit and system failure rates. This includes how often units and systems fail during the first year of operation as well as in later years.

:* '''Assist in deciding which product to purchase from a list of competing products.''' As a result, it is essential that reliability predictions be based on a common procedure.

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:# ''Unit'': Any assembly of devices. This may include, but is not limited to, circuit packs, modules, plug-in units, racks, power supplies, and ancillary equipment. Unless otherwise dictated by maintenance considerations, a unit will usually be the lowest level of replaceable assemblies/devices. The RPP is aimed primarily at reliability prediction of units.

:# ''Serial System'': Any assembly of units for which the failure of any single unit will cause a failure of the system.

== Data-driven reliability predictions ==

Data-driven models for reliability prediction utilise data acquired from tests to failure on electronic components by establishing relationships between the different variables presented in the data. As such relationships can be complex, data-driven models often require computations in high dimensions, which means that a large dataset is needed to optimize the output of the model.<ref>{{cite conference |last1=Ghrabli |first1=Mehdi|last2=Bouarroudj |first2=Mounira | author3=Chamoin, Ludovic|author4=Aldea, Emanuel |date=2024 |title=Hybrid modeling for remaining useful life prediction in power module prognosis |conference=2024 25th International Conference on Thermal Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE)|___location=Catania, Italy |publisher=IEEE|doi=10.1109/EuroSimE60745.2024.10491493 }}</ref>

== Physics-based reliability predictions ==

Physics based reliability predictions use physical equations and formulae to determine failure. This approach requires precise knowledge of the degradation process and the physical properties to ensure accuracy. These models often utilise numerical simulations to infer the quantities needed by the model.<ref>{{ cite journal | title=Physics-informed Markov chains for remaining useful life prediction of wire bonds in power electronic modules | journal=Microelectronics Reliability | year=2025 | last1=Ghrabli | author2=Bouarroudj, Mounira | author3=Chamoin, Ludovic|author4=Aldea, Emanuel | volume=167 | pages=1–12 | first1=Mehdi | article-number=115644 | doi=10.1016/j.microrel.2025.115644| bibcode=2025MiRe..16715644G | doi-access=free }}</ref>

== References ==