Simulation, modeling and optimization of devices, circuits and systems

对于电信电路的可制造性驱动设计而言，首通成功和上市时间至关重要。这就需要强大、准确和快速的计算机辅助设计（CAD）工具来进行组件建模、电路仿真和自动设计优化。直接基于物理和电磁学（EM）的模拟传统上是计算密集型的，但提供高精度，处理任意几何形状，并且对毫米波频率有效。它们在优化、统计设计和公差设计中的直接应用仍然具有挑战性。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学会2004年微波应用奖）将扩大为无线、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。该框架将基于我们在1993年首创的新型空间测绘优化技术，该技术已经在加拿大和国际工程界取得了各种重要的建模和设计成功。新算法有望在少量精细或高保真模型评估中提供使用顺序线性规划的经典直接优化所期望的精度。我们将继续与Kaj Madsen博士（丹麦技术大学）就空间映射算法的收敛理论进行合作。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，粗糙或代理设备模型。我们将继续探索与人工神经网络技术在设备建模方面的联系，例如神经空间映射（q.j Zhang）。我们将开发高效、稳健的算法，用于基于em的优化，利用空间映射、可用的伴随灵敏度、代理模型（John Dennis）和信任域方法。

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.