Simulation, modeling and optimization of devices, circuits and systems

首过成功和上市时间对于可制造性驱动的电信电路设计至关重要。 这就需要强大、准确和快速的计算机辅助设计（CAD）工具来进行元件建模、电路仿真和自动设计优化。 直接基于物理学和电磁学（EM）的仿真传统上是计算密集型的，但提供高精度，处理任意几何形状，并适用于毫米波频率。 它们在优化、统计设计和公差设计中的直接利用仍然具有挑战性。在可制造性、调谐和产量驱动优化的设计中心、公差和统计设计方面的开创性工作（IEEE MTT协会2004年微波应用奖）将扩展为无线、射频（RF）和微波元件和电路的计算机辅助工程的灵活理论和软件框架。 该框架将基于我们新颖的空间映射优化技术，该技术是我们在1993年率先推出的，已经在加拿大和国际工程界取得了多样化和重大的建模和设计成功。 新的算法预计将提供一些精细或高保真的模型评估，从经典的直接优化，使用顺序线性规划的准确性。我们将继续与Kaj Madsen博士（丹麦技术大学）合作，研究空间映射算法的收敛理论。 这将有助于工程实践者开发或区分粗糙的工程模型。 我们将开发原型软件引擎，利用全波EM模拟器和快速，经验，粗糙或替代设备模型。 我们将继续探索与人工神经网络技术的联系，用于设备建模，例如，神经空间映射（Q.J.Zhang）。 我们将开发有效的，强大的算法EM为基础的优化利用空间映射，可用的伴随灵敏度，代理模型（约翰丹尼斯）和信赖域方法。

First-pass success and time-to-market are crucial for manufacturability-driven design of telecommunication circuits.  This necessitates powerful, accurate and fast computer-aided design (CAD) tools for component modeling, circuit simulation and automated design optimization.  Simulations based directly on physics and electromagnetics (EM) are traditionally computationally intensive, but offer high accuracy, handle arbitrary geometrical shapes and are valid to millimeterwave frequencies.  Their direct exploitation in optimization, statistical design and design with tolerances remains challenging. Pioneering work in design centering, tolerance and statistical design for manufacturability, tuning and yield-driven optimization (IEEE MTT-Society 2004 Microwave Application Award) will be broadened into a flexible theoretical and software framework for computer-aided engineering of wireless, radio-frequency (RF) and microwave components and circuits.  The framework will be based on our novel space-mapping optimization technology, which we pioneered in 1993 and which has already resulted in diverse and significant modeling and design successes in the engineering community in Canada and internationally.  New algorithms are expected to deliver in a handful of fine or high-fidelity model evaluations the accuracy expected from classical direct optimization using sequential linear programming. We will continue to work with Dr. Kaj Madsen (Technical University Denmark) on the convergence theory for space-mapping algorithms.  This should help engineering practitioners in developing or discriminating between coarse engineering models.  We will develop prototype software engines which exploit both full-wave EM simulators and fast, empirical, coarse or surrogate device models.  We will continue to explore links with artificial neural network technology for device modeling, e.g., neuro space mapping (Q.J. Zhang).  We will develop efficient, robust algorithms for EM-based optimization exploiting space mapping, available adjoint sensitivities, surrogate models (John Dennis) and the trust region methodology.