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电源网络低频段S参数拓展方法

Low Frequency S-Parameter Extension Method of Power Network

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电源网络S参数与芯片电源模型(Chip Power Module,CPM)级联可实现电源时域噪声仿真,完成电源完整性设计签核.当下部分仿真工具在AC阻抗优化过程中导出的S参数存在低频段无法覆盖的问题,影响时域纹波仿真精度,如果重新对S参数进行提取,又会增加仿真时间降低仿真效率.针对AC阻抗优化过程中导出的S参数无法覆盖低频段的问题,提出了一种电源网络S参数低频段拓展方法,结合电压调节模块(Voltage Regulator Module,VRM)的R-L模型,推导出低频段的S参数可以借用抽取的S参数中最低频点处的S参数实现低频段S参数的拓展.仿真和实验结果表明,通过对低频段S参数进行拓展,电源时域纹波噪声仿真的精度提升31%.同时,低频段的S参数直接借用已抽取的S参数中低频点的数值无须重复提取,在8 GB内存的配置下,仿真时间节约14%左右,提高了仿真效率.
The cascaded S-parameter of the power network and the chip power model(CPM)can re-alize the time-domain noise simulation of the power supply,and complete the integrity design check of the power supply.At present,the S-parameter derived by some simulation tools in the AC imped-ance optimization process has the problem that the low frequency band cannot be covered,which af-fects the accuracy of the time-domain ripple simulation.If the S-parameter is extracted again,the simulation time will be increased and the simulation efficiency will be reduced.In order to solve this problem,this paper proposes a low frequency band expansion method of S-parameter of power net-work.Combining with the R-L model of voltage regulator module(VRM),it is deduced that the S-parameter of low frequency band can be extended by using the S-parameter at the lowest frequency point of the extracted S-parameter.The simulation and experimental results show that the accuracy of power ripple noise simulation in time domain is improved by 31%by expanding the S-parameter in low frequency band,and the S-parameter of the low frequency band directly borrows the value of the low frequency point from the extracted S parameter without repeated extraction.Under the configura-tion of 8 GB memory,the simulation time is saved by about 14%,and the simulation efficiency is improved.

S-parameters of power network impedancelow frequency band S-parameter expansiontime domain waveform simulation of power supplysimulation efficiency

赵豪兵、虎艳宾、李沛杰

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信息工程大学,河南郑州 450001

电源网络S参数 低频段S参数拓展 电源时域波形仿真 仿真效率

国家重点研发计划

2022YFB2901000

2024

信息工程大学学报
中国人民解放军信息工程大学科研部

信息工程大学学报

影响因子:0.276
ISSN:1671-0673
年,卷(期):2024.25(2)
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