Suboptimal design of power delivery networks (PDNs) may cause performance deterioration and severe functional failures on high-speed computer platforms. Voltage regulators (VRs) distribute controlled voltage in the PDN to the active devices, providing a steady power supply at a desired DC voltage level with an acceptable noise level or ripple. Unacceptable voltage drops can be caused by transient switching currents at the devices. Many decoupling capacitors are commonly used to lower the PDN impedance profile in order to reduce power supply noise and to supply fast transient current to switching devices. However, commercially available decoupling capacitors typically present large manufacturing variability. In this article, we first propose an optimization methodology that gradually finds the best compensation parameter values of a buck converter VR to meet suitable stability criteria. Simultaneously, the number of parallel decoupling capacitors in the PDN is minimized while meeting a frequency-domain impedance profile specification and a time-domain minimum voltage droop requirement under nominal parameter values. Finally, a statistical analysis, yield estimation, and yield optimization of the nominally optimized PDN subject to large decoupling capacitor tolerances is presented. We consider the impedance profile, transient voltage droop, and VR stability as the responses of interest for yield calculation.

As microprocessor design scales to the 10-nm technology and beyond, traditional pre- and post-silicon validation techniques are unsuitable to get a full system functional coverage. Physical complexity and extreme technology process variations severely limits the effectiveness and reliability of presilicon validation techniques. This scenario imposes the need of sophisticated post-silicon validation approaches to consider complex electromagnetic phenomena and large manufacturing fluctuations observed in actual physical platforms. One of the major challenges in electrical validation of high-speed input/output (HSIO) links in modern computer platforms lies in the physical layer (PHY) tuning process, where equalization techniques are used to cancel undesired effects induced by the channels. Current industrial practices for PHY tuning in HSIO links are very time consuming since they require massive laboratory measurements. An alternative is to use machine learning techniques to model the PHY, and then perform equalization using the resultant surrogate model. In this paper, a metamodeling approach based on neural networks is proposed to efficiently simulate the effects of a receiver equalizer PHY tuning settings. We use several design of experiments techniques to find a neural model capable of approximating the real system behavior without requiring a large amount of actual measurements. We evaluate the models performance by comparing with measured responses on a real server HSIO link.

As microprocessor design scales to nanometric technology, traditional post-silicon validation techniques are inappropriate to get a full system functional coverage. Physical complexity and extreme technology process variations introduce design challenges to guarantee performance over process, voltage, and temperature conditions. In addition, there is an increasingly higher number of mixed-signal circuits within microprocessors. Many of them correspond to high-speed input/output (HSIO) links. Improvements in signaling methods, circuits, and process technology have allowed HSIO data rates to scale beyond 10 Gb/s, where undesired effects can create multiple signal integrity problems. With all of these elements, post-silicon validation of HSIO links is tough and time-consuming. One of the major challenges in electrical validation of HSIO links lies in the physical layer (PHY) tuning process, where equalization techniques are used to cancel these undesired effects. Typical current industrial practices for PHY tuning require massive lab measurements, since they are based on exhaustive enumeration methods. In this work, direct and surrogate-based optimization methods, including space mapping, are proposed based on suitable objective functions to efficiently tune the transmitter and receiver equalizers. The proposed methodologies are evaluated by lab measurements on realistic industrial post-silicon validation platforms, confirming dramatic speed up in PHY tuning and substantial performance improvement.