Recent Advances in EM-Based Yield Estimation

Zhang, Jianan; Zhang, Chao; Feng, Feng; Zhang, Wei; Zhang, Qi‐Jun

doi:10.1109/nemo.2018.8503128

Cited by 7 publications

(8 citation statements)

References 12 publications

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“…Traditional Monte Carlo (MC) method for EMbased yield estimation is computationally expensive as a large number of EM simulations are required to achieve desired yield estimation accuracy. Recently, the polynomial chaos expansion (PCE) approach [15] has emerged as a powerful tool for statistical analysis and yield estimation in microwave design [16]- [20]. Existing studies have shown that PCE has significant benefits over the traditional Monte Carlo analysis in terms of reduced computational costs and shorter CPU time for yield estimation of microwave structures [20].…”

Section: Introductionmentioning

confidence: 99%

Parallel Space-Mapping Based Yield-Driven EM Optimization Incorporating Trust Region Algorithm and Polynomial Chaos Expansion

Zhang

Feng

et al. 2019

IEEE Access

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Space mapping (SM) methodology has been recognized as a powerful tool for accelerating electromagnetic (EM)-based yield optimization. This paper proposes a novel parallel space-mapping based yield-driven EM optimization technique incorporating trust region algorithm and polynomial chaos expansion (PCE). In this technique, a novel trust region algorithm is proposed to increase the robustness of the SM surrogate in each iteration during yield optimization. The proposed algorithm updates the trust radius of each design parameter based on the effectiveness of minimizing the l 1 objective function using the surrogate, thereby increasing the robustness of the SM surrogate. Moreover, for the first time, parallel computation method is incorporated into SM-based yield-driven design to accelerate the overall yield optimization process of microwave structures. The use of parallel computation allows the surrogate developed in the proposed technique to be valid in a larger neighborhood than that in standard SM, consequently increasing the speed of finding the optimal yield solution in SM-based yield-driven design. Lastly, the PCE approach is incorporated into the proposed technique to further speed up yield verification on the fine model. Compared with the standard SM-based yield optimization technique with sequential computation, the proposed technique achieves a higher yield increase with shorter CPU time by reducing the number of SM iterations. The proposed technique is illustrated by two microwave examples. INDEX TERMSElectromagnetic optimization, parallel computation, polynomial chaos expansion, space mapping, trust region, yield optimization. JIANAN ZHANG (S'15) was born in Tieling, Liaoning, China, in 1991. He received the B.Eng. degree from Tianjin University, Tianjin, China, in 2013, where he is currently pursuing the Ph.D. degree with the School of Microelectronics. He is also in a cotutelle Ph.D. program of the Department of Electronics, Carleton University, Ottawa, ON, Canada. His research interests include statistical modeling, EM-based optimization of microwave structures, uncertainty quantification, computational electromagnetics, and space mapping-based EM optimization.

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Section: Introductionmentioning

confidence: 99%

Parallel Space-Mapping Based Yield-Driven EM Optimization Incorporating Trust Region Algorithm and Polynomial Chaos Expansion

Zhang

Feng

et al. 2019

IEEE Access

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“…In other words, the total number of PCE models is equal to the total number of frequency points in the whole frequency range of interest. Then, an MC analysis needs to be performed on the PCE models taking into consideration all the design specifications [95]. We summarize the yield verification on the fine model via PCE at iteration k as follows:…”

Section: Incorporating the Pce Approach For Yield Verification On Thementioning

confidence: 99%

“…Traditional Monte Carlo (MC) method for EM-based yield estimation is computationally expensive as a large number of EM simulations are required to achieve desired yield estimation accuracy. Recently, the polynomial chaos expansion (PCE) approach[36] has emerged as a powerful tool for statistical analysis and yield estimation in microwave design[37]-[95]. Existing studies have shown that PCE has significant benefits over the traditional Monte Carlo analysis in terms of reduced computational costs and shorter CPU time for yield estimation of microwave structures[95].…”

mentioning

confidence: 99%

“…Recently, the polynomial chaos expansion (PCE) approach[36] has emerged as a powerful tool for statistical analysis and yield estimation in microwave design[37]-[95]. Existing studies have shown that PCE has significant benefits over the traditional Monte Carlo analysis in terms of reduced computational costs and shorter CPU time for yield estimation of microwave structures[95]. However, how to combine SM, the parallel computation method, and PCE to facilitateEM-based yield optimization still remain an open subject in the literature.…”

mentioning

confidence: 99%

“…probability density function (PDF) of the transformed parameters ξ. Numerical quadrature based on sparse grid techniques[95] are typically used to evaluate the multi-dimensional integrations in(4.26). Let N sg be the number of samples in sparse grid techniques.…”

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confidence: 99%

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Advanced Parametric Modeling and Yield Optimization Approaches for Microwave Passive Components

Zhang¹

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Parametric modeling and yield optimization techniques play important roles in electromagnetic (EM)-based microwave component design. In the first part of this thesis, we propose a novel training approach for parametric modeling of microwave passive components with respect to changes in geometrical parameters using matrix Padé via Lanczos (MPVL) and EM sensitivities. In the proposed approach, the EM responses of passive components versus frequency are represented by polezero-gain transfer functions. The relationships between the poles/zeros/gain in the transfer function and geometrical variables are learnt by neural networks. A novel

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Rapid Yield Estimation of Microwave Passive Components Using Model-Order Reduction Based Neuro-Transfer Function Models

Zhang

Feng

Zhang

2021

IEEE Microw. Wireless Compon. Lett.

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Recent Advances in EM-Based Yield Estimation

Cited by 7 publications

References 12 publications

Parallel Space-Mapping Based Yield-Driven EM Optimization Incorporating Trust Region Algorithm and Polynomial Chaos Expansion

Parallel Space-Mapping Based Yield-Driven EM Optimization Incorporating Trust Region Algorithm and Polynomial Chaos Expansion

Advanced Parametric Modeling and Yield Optimization Approaches for Microwave Passive Components

Rapid Yield Estimation of Microwave Passive Components Using Model-Order Reduction Based Neuro-Transfer Function Models

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