There can be little doubt that the Monte Carlo method for semiconductor device simulation has enormous power as a research tool. It represents a detailed physical model of the semiconductor material(s), and provides a high degree of insight into the microscopic transport processes. However, if the authority ascribed to Monte Carlo models of devices at 1/spl mu/m feature size is to be maintained for devices below O.1/spl mu/m, modelling of the fundamental physics must be further improved. And if the Monte Carlo method is to be successful as a semiconductor device design tool, the device model must be made more realistic. Success in the industrial sector depends on this, but also on achieving fast run-times optimisation - where the scope and need for ingenuity is now greatest.

The Monte Carlo method for semiconductor device simulation

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Моделирование свойств двойного волноводного тройника в CST Microwave Studio

Partial differential equations (PDEs) are widely used across the physical and computational sciences. Decades of research and engineering went into designing fast iterative solution methods. Existing solvers are general purpose, but may be sub-optimal for specific classes of problems. In contrast to existing hand-crafted solutions, we propose an approach to learn a fast iterative solver tailored to a specific domain. We achieve this goal by learning to modify the updates of an existing solver using a deep neural network. Crucially, our approach is proven to preserve strong correctness and convergence guarantees. After training on a single geometry, our model generalizes to a wide variety of geometries and boundary conditions, and achieves 2-3 times speedup compared to state-of-the-art solvers.

Learning Neural PDE Solvers with Convergence Guarantees

Machine learning is rapidly becoming a core technology for scientific computing, with numerous opportunities to advance the field of computational fluid dynamics. In this Perspective, we highlight some of the areas of highest potential impact, including to accelerate direct numerical simulations, to improve turbulence closure modeling, and to develop enhanced reduced-order models. We also discuss emerging areas of machine learning that are promising for computational fluid dynamics, as well as some potential limitations that should be taken into account. 

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Enhancing computational fluid dynamics with machine learning

In this paper I-V method-based impedance measurement of power distribution network (PDN) in printed circuit board (PCB) technique is proposed and compared to the traditional VNA method [1]. A I-V method-based probe is designed and verified by comparing the performance with VNA method. To de-embed the parasitic problems, a special I-V probe calibration method is created according to the characteristics of the probe.

I-V Method Based PDN Impedance Measurement Technique and Associated Probe Design

A method is proposed to predict the worst case shielding effectiveness of a board level shield (BLS). This method estimates the shielding effectiveness (SE) of the shield below and above the first resonance of a cavity formed by the BLS. The apertures of the BLS are modelled as antennas with known radiation efficiency. The estimation of the maximum field inside the cavity is used to calculate the magnetic currents driving the antennas above the first resonance of the cavity. Below the first resonance the field inside the cavity is estimated using the resonant LC circuit model. An approximated formula for the radiation of the uniformly placed apertures is developed. The worst case prediction is validated by the full wave simulation and also in measurement on a real BLS.

Worst case prediction of shielding effectiveness of board level shields

Fast impedance prediction for power distribution network using deep learning

This paper proposes a one-dimensional boundary integration approach that can efficiently calculate the edge electromagnetic radiation from a printed circuit board (PCB) with a typical stripline excitation. The effect of shielding vias is also considered. The method takes much less time than the conventional numerical method for computation but obtains a perfect agreement with full-wave simulation results.

Fast Calculation of PCB Electromagnetic Radiation Using 1D Boundary Integration

Simulating the dynamic characteristics of a PN junction at the microscopic level requires solving the Poisson's equation at every time step. Solving at every time step is a necessary but time-consuming process when using the traditional finite difference (FDM) approach. Deep learning is a powerful technique to fit complex functions. In this work, deep learning is utilized to accelerate solving Poisson's equation in a PN junction. The role of the boundary condition is emphasized in the loss function to ensure a better fitting. The resulting I-V curve for the PN junction, using the deep learning solver presented in this work, shows a perfect match to the I-V curve obtained using the finite difference method, with the advantage of being 10 times faster at every time step.

Ling Zhang

Papers

I-V Method Based PDN Impedance Measurement Technique and Associated Probe Design

Worst case prediction of shielding effectiveness of board level shields

Fast impedance prediction for power distribution network using deep learning

Fast Calculation of PCB Electromagnetic Radiation Using 1D Boundary Integration

Solving Poisson's Equation using Deep Learning in Particle Simulation of PN Junction