A Novel Dynamic Neuro-Space Mapping Approach for Nonlinear Microwave Device Modeling

Zhu, Lin; Zhang, Qi‐Jun; Liu, Kaihua; Ma, Yongtao; Peng, Bo; Yan, Shuxia

doi:10.1109/lmwc.2016.2516761

Cited by 46 publications

(48 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Neuro-SM, neural networks are used to automatically map and modify an existing equivalent circuit model also called coarse model to a desired/accurate model through a process named training. In order to fulfill the needs of the increased modeling complexity and the industry's increasing need for tighter accuracy, several improvements on the basis of [10] were subsequently studied to enhance the modeling accuracy and efficiency, such as Neuro-SM with the output mapping [13], dynamic Neuro-SM [14], and analytical Neuro-SM with sensitivity analysis [15]. Neuro-SM with the output mapping [13] was introduced, through incorporation of a new output/current mapping, for modeling of microwave devices.…”

Section: Introductionmentioning

confidence: 99%

“…Compared to the Neuro-SM presented in [10], Neuro-SM with the output mapping is more suitable for modeling nonlinear devices with more nonlinearity due to the additional and useful degrees of freedom from the output mapping neural network. In order to accurately model nonlinear devices which have higher order dynamic effects (e.g., capacitive effect or non-quasi-static effect) than that of the coarse model, dynamic Neuro-SM was introduced [14]. However, when the modeling devices have both more nonlinearity and high order dynamics, in such case, even though existing Neuro-SM [13,14] is used to map the coarse model towards the device data, the match between the trained Neuro-SM models and the device data may be still not good enough.…”

Section: Introductionmentioning

confidence: 99%

“…In order to accurately model nonlinear devices which have higher order dynamic effects (e.g., capacitive effect or non-quasi-static effect) than that of the coarse model, dynamic Neuro-SM was introduced [14]. However, when the modeling devices have both more nonlinearity and high order dynamics, in such case, even though existing Neuro-SM [13,14] is used to map the coarse model towards the device data, the match between the trained Neuro-SM models and the device data may be still not good enough. More effective Neuro-SM methods need to be investigated to overcome the accuracy limit of the Neuro-SM presented in [13,14].…”

Section: Introductionmentioning

confidence: 99%

“…However, when the modeling devices have both more nonlinearity and high order dynamics, in such case, even though existing Neuro-SM [13,14] is used to map the coarse model towards the device data, the match between the trained Neuro-SM models and the device data may be still not good enough. More effective Neuro-SM methods need to be investigated to overcome the accuracy limit of the Neuro-SM presented in [13,14].…”

Section: Introductionmentioning

confidence: 99%

“…This paper is a further expansion of the work in [13,14]. Compared to [13] where only static mapping is used, the proposed technique is more suitable for modeling nonlinear devices with higher order dynamic effects and non-quasi-static effect that may be missing in the coarse model due to inclusion of dynamic mapping.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A general neuro-space mapping technique for microwave device modeling

Zhu

Zhao

et al. 2018

J Wireless Com Network

Self Cite

View full text Add to dashboard Cite

Accurate modeling of nonlinear microwave devices is critical for reliable design of microwave circuit and system. In this paper, a more general neuro-space mapping (Neuro-SM) method is proposed to fulfill the needs of the increased modeling complexity. The proposed technique retains the capability of the existing dynamic Neuro-SM in modifying the dynamic voltage relationship between the coarse model and the desired model. The proposed Neuro-SM also considers dynamic current mapping besides voltage mappings. In this way, the proposed Neuro-SM generalizes the previously published Neuro-SM methods and has the potential to produce a more accurate model of microwave devices with more dynamics and nonlinearity. A new formulation and new sensitivity analysis technique are derived to train the general Neuro-SM with dc, small-, and large-signal data. A new gradient-based training algorithm is also proposed to speed up the training. The validity and efficiency of the general Neuro-SM method are demonstrated through a real 2 × 50 μm GaAs pseudomorphic high-electron mobility transistor (pHEMT) modeling example. The proposed general Neuro-SM model can be implemented into circuit simulators conveniently.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A general neuro-space mapping technique for microwave device modeling

Zhu

Zhao

et al. 2018

J Wireless Com Network

Self Cite

View full text Add to dashboard Cite

show abstract

Dynamic behavioral modeling of nonlinear circuits using a novel recurrent neural network technique

Naghibi

Sadrossadat

2019

Circuit Theory & Apps

View full text Add to dashboard Cite

In this paper, a new method called local-global feedback recurrent neural network (LGFRNN) is proposed for dynamic behavioral modeling of nonlinear circuits. The structure of the proposed method is based on recurrent neural network and constructed by time-delayed local and global feedbacks. Adding time-delayed feedbacks has a great impact on the learning capability of previous neural network-based methods. Moreover, time-delayed local feedbacks alleviate the problem of slow convergency of the conventional neural network-based methods in the training phase. The proposed LGFRNN can be trained only by having sampled input-output waveforms of the original circuit without knowing the internal details of the circuit. A training algorithm based on real-time recurrent learning (RTRL) is used to train LGFRNN. After the training phase, the proposed LGFRNN provides accurate macromodel of a nonlinear circuit.The proposed method is more accurate compared with the conventional neural-based models (which do not benefit from time-delayed local-global feedbacks) and also significantly reduces the training time of the conventional models. Moreover, proposed LGFRNN is faster than the existing models in simulation tools. The validity of the proposed method is verified by time-domain modeling of three nonlinear devices including commercial TI's SN74AHCT540 device, five-stage complementary metal-oxide-semiconductor (CMOS) receiver, and commercial TI's LM324 power amplifier.

show abstract

Modeling and implementation of a novel active voltage balancing circuit using deep recurrent neural network with dropout regularization

Noohi

Faraji

Sadrossadat

et al. 2022

Circuit Theory & Apps

View full text Add to dashboard Cite

Recurrent neural networks (RNN) emerged as powerful tools to model and analyze the nonlinear behavior of electronic circuits accurately and quickly.Efforts to improve the accuracy of RNN will lead to the design of better-quality products, which is essential in various fields such as the design of energy harvesting (EH) systems. EH techniques can provide the electrical energy needed for low-power electronics without the need for a battery or with minimal dependency. Due to the importance of the active voltage balancing circuit in EH systems, we have proposed a new macromodeling method called dropout local-feedback deep recurrent neural network (Dropout-LFDRNN) to model and analyze this circuit along with two other nonlinear circuits asexamples. This technique is an advance over the LFDRNN macromodeling method, and based on the obtained results from the measurements, we were able to build a fast macromodel for the active balancing circuit, which outperforms conventional deep recurrent neural network modeling method in terms of accuracy without sacrificing speed. It is worth mentioning that this proposed technique in this paper can be considered a viable approach for modeling and analysis of nonlinear electronic components and circuits. In addition to the advantage of generating a more accurate model, the model based on the Dropout-LFDRNN approach is much faster than the existing transistor-level models.

show abstract

A Novel Dynamic Neuro-Space Mapping Approach for Nonlinear Microwave Device Modeling

Cited by 46 publications

References 8 publications

A general neuro-space mapping technique for microwave device modeling

A general neuro-space mapping technique for microwave device modeling

Dynamic behavioral modeling of nonlinear circuits using a novel recurrent neural network technique

Modeling and implementation of a novel active voltage balancing circuit using deep recurrent neural network with dropout regularization

Contact Info

Product

Resources

About