含大规模端口的MEMS宏模型提取及角度参数化

针对含大规模输入输出端口的MEMS结构,提出分块叠加Arnoldi方法提取其宏模型,针对MEMS器件中广泛存在几何形状相同而初始方位不同的结构,采用矩阵坐标变换实现宏模型的角度参数化,解决宏模型的重复提取问题。最后,结合一种微型可编程光栅的设计验证了宏模型的效率与精度,与有限元仿真结果进行比较,基于宏模型的MEMS器件系统级仿真结果相对误差小于2%,时域仿真速度提高了45倍。     

Guesstimation

Macroeconomic model builders attempting to construct forecasting models frequently face constraints of data scarcity in terms of short time series of data, and also of parameter non‐constancy and underspecification. Hence, a realistic alternative is often to guess rather than to estimate parameters of such models. This paper concentrates on repetitive guessing (drawing) parameters from iteratively changing distributions, with the straightforward objective function being that of minimization of squares of ex‐post prediction errors, weighted by penalty weights and subject to a learning process. The examples are those of a Monte Carlo analysis of a regression problem and of a dynamic disequilibrium model. It is also an example of an empirical econometric model of the Polish economy. Copyright © 2002 John Wiley & Sons, Ltd.     

Computation speedup in the dynamic simulation of MEMS by macromodels

A doubly clamped microbeam actuated by electrostatic force with squeezed gas film damping is a well-known and standard micro-device in microelectromechanical system (MEMS) for many researchers to demonstrate how reduced-order dynamic macromodel is an effective way to faithfully capture the device behaviors. However it still takes time to directly recompute the time-dependant nonlinear terms in macromodels which are generated by a proper orthogonal decomposition (POD) method with Galerkin procedure at every time step during the macromodel simulation. This paper proposes two methods for speeding up the computation of macromodel simulations. In the first method, the computation speedup is achieved based on the concept of precomputation upon the basis functions are available. In the second method, cubic splines approximation is used to interpolate the basis functions and their first and second derivatives, and spatial integration is performed by application of the Gaussian quadrature. Numerical results show both methods could enhance the efficiency of the macromodel simulation compared with our previous computation results