Surrogate-Based Analysis and Design Optimization of Power Delivery Networks

As microprocessor architectures continue to increase computing performance under low-energy consumption, the combination of signal integrity, electromagnetic interference, and power delivery (PD) is becoming crucial in the computer industry. In that context, PD engineers make use of complex and computationally expensive models that impose time-consuming industrial practices to reach an adequate PD design. In this article, we propose a general surrogate-based methodology for fast and reliable analysis and design optimization of PD 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 fourth generation server CPU motherboard. We next propose a black box PDN surrogate modeling methodology for efficient and reliable PD 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 (VR) with dual power rail remote sensing intended for communications and storage applications, by finding optimal sensing resistors and loading conditions; and a multiphase VR from a fifth-generation Intel server motherboard, by finding optimal compensation settings to reduce the number of bulk capacitors without losing CPU performance.