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Integrated power semiconductors are often used for applications with cyclic on-chip power dissipation. This leads to repetitive self-heating and thermo-mechanical stress, causing fatigue on the on-chip metallization and possibly destruction by short circuits. Because of this, an accurate simulation of the thermo-mechanical stress is needed already during the design phase to ensure that lifetime requirements are met. However, a detailed thermo mechanical simulation of the device, including the on-chip metallization is prohibitively time-consuming due to its complex structure, typically consisting of many thin metal lines with thousands of vias. This paper introduces a two-step approach as a solution for this problem. First, a simplified but fast simulation is performed to identify the device parts with the highest stress. After, precise simulations are carried out only for them. The applicability of this method is verified experimentally for LDMOS transistors with different metal configurations. The measured lifetimes and failure locations correlate well with the simulations. Moreover, a strong influence of the layout of the on-chip metallization lifetime was observed. This could also be explained with the simulation
method.
This paper investigates the electrothermal stability and the predominant defect mechanism of a Schottky gate AlGaN/GaN HEMT. Calibrated 3-D electrothermal simulations are performed using a simple semiempirical dc model, which is verified against high-temperature measurements up to 440°C. To determine the thermal limits of the safe operating area, measurements up to destruction are conducted at different operating points. The predominant failure mechanism is identified to be hot-spot formation and subsequent thermal runaway, induced by large drain–gate leakage currents that occur at high temperatures. The simulation results and the high temperature measurements confirm the observed failure patterns.
We present a fully automatic approach to real-time 3D face reconstruction from monocular in-the-wild videos. With the use of a cascaded-regressor-based face tracking and a 3D morphable face model shape fitting, we obtain a semidense 3D face shape. We further use the texture information from multiple frames to build a holistic 3D face representation from the video footage. Our system is able to capture facial expressions and does not require any person specific training. We demonstrate the robustness of our approach on the challenging 300 Videos in the Wild (300- VW) dataset. Our real-time fitting framework is available as an open-source library at http://4dface.org.