A μ-GA Oriented ANN-Driven: Parameter Extraction of 5G CMOS Power Amplifier

IF 2.8 2区 工程技术 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Tahesin Samira Delwar;Abrar Siddique;Unal Aras;Yangwon Lee;Jee Youl Ryu
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引用次数: 0

Abstract

This article introduces a novel method for extracting crucial parameters from a fifth-generation (5G) CMOS power amplifier (PA) operating at 24 GHz. The proposed method, micro-genetic algorithm artificial neural network ( $\mu $ -GAANN), presents an innovative synergy between $\mu $ -GA and ANN, enabling the accurate determination of crucial PA (circuit components) parameters. The $\mu $ -GAANN model has a fixed and robust stimulation function ( ${F} {_{\text {SF}}}$ and ${R} {_{\text {SF}}}$ ). ANNs are trained to approximate the parameter extraction process based on input-output data generated from the $\mu $ -GA. The proposed $\mu $ -GA incorporates the arithmetic crossover and nonuniform mutation; thus, several parameters of the ANN network are tweaked. Moreover, ANN parameters are enhanced by using $\mu $ -GA to achieve an optimal PA design in a shorter period of time. To verify the proposed $\mu $ -GAANN, we have also compared the training time with particle swarm optimization (PSO) employed in ANN, i.e., PSOANN. Besides, a derivative superposition (DS) linearization technique is used in the PA circuit, along with input load splits (I-LSs) to solve the low input impedance problem of conventional DS. To design a PA, the proposed $\mu $ -GAANN outperforms the traditional feedforward artificial neural networks (TFFANN). Using $\mu $ -GAANN, the PA’s simulated S21 is 25 dB, while the measured S21 is 21.2 dB. With traditional TFFANN, we observe a simulated gain of 24.1 dB for the PA. We achieved a simulated gain of 23.2 dB of the PA without using ANNs. The measured results of the $P {_{\text {sat}}}$ and PAE of the PA with $\mu $ -GAANN are 9.8 dBm and 32.1%, respectively. Also, a measured PA achieves a high third-order-input-intercept point (IIP3) of 14.1 dBm. The core chip area of the PA is 0.35 mm2.
面向ANN驱动的$\mu$-GA:5G CMOS 功率放大器的参数提取
本文介绍了一种从工作频率为 24 GHz 的第五代(5G)CMOS 功率放大器(PA)中提取关键参数的新方法。所提出的方法,即微遗传算法人工神经网络($\mu $ -GAANN),在$\mu $ -GA和ANN之间实现了创新性的协同作用,从而能够准确确定关键功率放大器(电路元件)参数。$\mu $ -GAANN 模型有一个固定且稳健的刺激函数(${F} {_{\text {SF}}$ 和 ${R} {_{\text {SF}}$ )。根据 $\mu $ -GA 生成的输入输出数据,训练 ANN 近似参数提取过程。所提出的 $\mu $ -GA 包含算术交叉和非均匀突变;因此,ANN 网络的几个参数会被调整。此外,通过使用$\mu $ -GA,增强了ANN参数,从而在更短的时间内实现了最佳功率放大器设计。为了验证所提出的 $\mu $ -GAANN 方法,我们还将其训练时间与 ANN 中使用的粒子群优化(PSO)方法(即 PSOANN)进行了比较。此外,我们还在功率放大器电路中使用了导数叠加(DS)线性化技术和输入负载分割(I-LS),以解决传统 DS 的低输入阻抗问题。在设计功率放大器时,所提出的 $\mu $ -GAANN 优于传统的前馈人工神经网络(TFFANN)。使用 $\mu $ -GAANN,功率放大器的模拟 S21 为 25 dB,而测量 S21 为 21.2 dB。使用传统的 TFFANN,我们观察到功率放大器的模拟增益为 24.1 dB。在不使用 ANN 的情况下,我们实现了功率放大器 23.2 dB 的模拟增益。使用 $\mu $ -GAANN 的功率放大器的 $P {_{text\ {sat}}$ 和 PAE 的测量结果分别为 9.8 dBm 和 32.1%。此外,测量的功率放大器还达到了 14.1 dBm 的高三阶输入截点 (IIP3)。功率放大器的核心芯片面积为 0.35 平方毫米。
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来源期刊
CiteScore
6.40
自引率
7.10%
发文量
187
审稿时长
3.6 months
期刊介绍: The IEEE Transactions on VLSI Systems is published as a monthly journal under the co-sponsorship of the IEEE Circuits and Systems Society, the IEEE Computer Society, and the IEEE Solid-State Circuits Society. Design and realization of microelectronic systems using VLSI/ULSI technologies require close collaboration among scientists and engineers in the fields of systems architecture, logic and circuit design, chips and wafer fabrication, packaging, testing and systems applications. Generation of specifications, design and verification must be performed at all abstraction levels, including the system, register-transfer, logic, circuit, transistor and process levels. To address this critical area through a common forum, the IEEE Transactions on VLSI Systems have been founded. The editorial board, consisting of international experts, invites original papers which emphasize and merit the novel systems integration aspects of microelectronic systems including interactions among systems design and partitioning, logic and memory design, digital and analog circuit design, layout synthesis, CAD tools, chips and wafer fabrication, testing and packaging, and systems level qualification. Thus, the coverage of these Transactions will focus on VLSI/ULSI microelectronic systems integration.
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