Surrogate-based Analysis and Design Optimization of Power Delivery Networks

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dc.contributor.authorLeal-Romo, Felipe J.
dc.contributor.authorRayas-Sánchez, José E.
dc.contributor.authorChávez-Hurtado, José L.
dc.date.accessioned2021-02-12T01:33:59Z
dc.date.available2021-02-12T01:33:59Z
dc.date.issued2020-03-24
dc.descriptionAs microprocessor architectures continue to increase computing performance under low-energy consumption, the combination of signal integrity, electromagnetic interference, and power delivery is becoming crucial in the computer industry. In this context, power delivery engineers make use of complex and computationally expensive models that impose time-consuming industrial practices to reach an adequate power delivery design. In this paper, we propose a general surrogate-based methodology for fast and reliable analysis and design optimization of power delivery networks (PDN). We first formulate a generic surrogate model methodology exploiting passive lumped models optimized by parameter extraction to fit PDN impedance profiles. This PDN modeling formulation is illustrated with industrial laboratory measurements of a 4th generation server CPU motherboard. We next propose a black box PDN surrogate modeling methodology for efficient and reliable power delivery design optimization. To build our black box PDN surrogate, we compare four metamodeling techniques: support vector machines, polynomial surrogate modeling, generalized regression neural networks, and Kriging. The resultant best metamodel is then used to enable fast and accurate optimization of the PDN performance. Two examples validate our surrogate-based optimization approach: a voltage regulator with dual power rail remote sensing intended for communications and storage applications, by finding optimal sensing resistors and loading conditions; and a multiphase voltage regulator from a 6th generation Intel® server motherboard, by finding optimal compensation settings to reduce the number of bulk capacitors without losing CPU performance.es_MX
dc.description.sponsorshipITESO, A.C.es_MX
dc.identifier.citationF. J. Leal-Romo, J. E. Rayas-Sánchez, and J. L. Chávez-Hurtado, “Surrogate-based analysis and design optimization of power delivery networks,” IEEE Trans. Electromagnetic Compatibility, vol. 62, no. 6, pp. 2528-2537, Dec. 2020. (p-ISSN: 0018-9375; e-ISSN: 1558-187X; published online: 24 Mar. 2020; DOI: 10.1109/TEMC.2020.2973946)es_MX
dc.identifier.issn0018-9375
dc.identifier.urihttps://hdl.handle.net/11117/6484
dc.language.isoenges_MX
dc.publisherIEEEes_MX
dc.rights.urihttp://quijote.biblio.iteso.mx/licencias/CC-BY-NC-ND-2.5-MX.pdfes_MX
dc.subjectImpedance Profilees_MX
dc.subjectIP Protectiones_MX
dc.subjectMetamodelses_MX
dc.subjectMicroprocessores_MX
dc.subjectMotherboardes_MX
dc.subjectOptimizationes_MX
dc.subjectPower Delivery Networkes_MX
dc.subjectPower Integrityes_MX
dc.subjectSurrogate Modelinges_MX
dc.subjectVoltage Regulatores_MX
dc.titleSurrogate-based Analysis and Design Optimization of Power Delivery Networkses_MX
dc.typeinfo:eu-repo/semantics/articlees_MX
dc.type.versioninfo:eu-repo/semantics/submittedVersiones_MX

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