Design and Optimization of Microwave Circuits and Systems Using Artificial Intelligence Techniques
Design and Optimization of Microwave Circuits and Systems Using Artificial Intelligence Techniques
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利用人工智能技术设计和优化微波电路和系统
DOI：
10.23919/ropaces.2018.8364141
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发表时间：
2005
期刊：
影响因子：
--
通讯作者：
R. Pratap
中科院分区：
文献类型：
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作者：
R. Pratap
本文提出了一种结合神经网络和遗传算法的微波设计新方法。在该方法中，根据实验数据开发了精确的神经网络模型。该神经网络模型用于执行敏感性分析并导出响应面。然后应用一种创新技术，将遗传算法与神经网络模型结合起来以协助合成和优化。所提出的方法用于对高达 35 GHz 的倒装芯片互连的电路参数进行建模和分析，以及用于 1.9 GHz 和 2.4 GHz 的多层电感器和电容器的设计。该方法还用于合成 40-60 GHz 范围内的毫米波低通滤波器。根据神经遗传设计方法预测的布局参数获得的器件产生的电响应接近于期望值（准确度为 95%）。所提出的方法还实现了加权优先级方案以考虑微波设计中的权衡。该方案用于合成 5-6 GHz 范围内 802.11a 和 HIPERLAN 无线 LAN 应用的带通滤波器。
这项研究还开发了一种新颖的神经遗传设计中心方法，用于提高微波器件和电路的可制造性和设计。神经网络模型用于使用蒙特卡罗方法计算产量。然后使用遗传算法进行产量优化。该方法已用于提高SiGe异质结双极晶体管和毫米波压控振荡器的成品率。它显着提高了 SiGe HBT（从 25% 到 75%）和 VCO（从 8% 到 85%）的良率。所提出的方法可以扩展到器件、电路、封装和系统级集成协同设计，因为它可以处理大量设计变量，而无需对组件行为进行任何假设。微波社区可以使用所提出的算法来设计和优化微波电路和系统，精度更高，同时消耗更少的计算时间。
In this thesis, a new approach combining neural networks and genetic algorithms is presented for microwave design. In this method, an accurate neural network model is developed from the experimental data. This neural network model is used to perform sensitivity analysis and derive response surfaces. An innovative technique is then applied in which genetic algorithms are coupled with the neural network model to assist in synthesis and optimization. The proposed method is used for modeling and analysis of circuit parameters for flip chip interconnects up to 35 GHz, as well as for design of multilayer inductors and capacitors at 1.9 GHz and 2.4 GHz. The method was also used to synthesize mm wave low pass filters in the range of 40–60 GHz. The devices obtained from layout parameters predicted by the neuro-genetic design method yielded electrical response close to the desired value (95% accuracy). The proposed method also implements a weighted priority scheme to account for tradeoffs in microwave design. This scheme was implemented to synthesize bandpass filters for 802.11 a and HIPERLAN wireless LAN applications in the range of 5–6 GHz.
This research also develops a novel neuro-genetic design centering methodology for yield enhancement and design for manufacturability of microwave devices and circuits. A neural network model is used to calculate yield using Monte Carlo methods. A genetic algorithm is then used for yield optimization. The proposed method has been used for yield enhancement of SiGe heterojunction bipolar transistor and mm wave voltage-controlled oscillator. It results in significant yield enhancement of the SiGe HBTs (from 25% to 75%) and VCOs (from 8% to 85%). The proposed method can be extended for device, circuit, package, and system level integrated co-design since it can handle a large number of design variables without any assumptions about the component behavior. The proposed algorithm could be used by microwave community for design and optimization of microwave circuits and systems with greater accuracy while consuming less computational time.