物理指导的神经替代模型与基于粒子群的多目标优化,用于准同轴TSV互连设计
Zheng Liu1, Guangbao Shan1, Zeyu Chen1
1Faculty of Integrated Circuit, Xidian University, Xi'an 710071, China.
Micromachines
|October 29, 2025
概括
这项研究引入了一种新的受物理约束的替代模型,用于在射频 (RF) 微系统中准确的电磁建模. 该模型确保了因果关系和被动性等物理约束,改善了高频信号完整性.
科学领域:
- 电气工程和计算机科学
- 电磁学和计算机电磁学
- 微系统和射频工程
背景情况:
- 精确的电磁 (EM) 建模对于可重新配置的射频 (RF) 微系统中的高频信号完整性至关重要.
- 传统的神经网络替代模型往往忽视了诸如因果关系和被动性之类的物理约束,从而限制了它们在现实世界的适用性.
- 现有的方法难以平衡数值准确性与必要的物理原理,以可靠地预测射频微系统的性能.
研究的目的:
- 开发一个物理约束的神经转移替代模型,用于在射频微系统中准确的S参数预测.
- 通过使用专门的规范化术语,在代用模型中强制执行因果关系和被动性约束.
- 使用粒子群优化 (PSO) 框架优化准同轴TSV复合结构的性能.
主要方法:
- 建议采用带宽输出架构的神经传输替代模型来预测从1-50 GHz的S参数.
- 在模型的训练阶段,因果关系和被动性通过规范化术语被强制执行.
- 一个多目标的PSO框架,包括固定重量规范化和线性下降的惯性重量,优化S11,S21和S22参数.
主要成果:
- 这种受物理限制的模型在1-50 GHz带宽上实现了直接的S参数预测.
- 针对TSV复合结构的优化结构参数达到S11 (-25 dB),S21 (-0.54 dB) 和S22 (-24 dB) 的目标值.
- 预测到模拟的偏差低于1dB,测试组的平均预测误差为2.11%.
结论:
- 拟议的物理约束的神经转移替代模型有效地预测S参数,同时坚持关键的物理约束.
- 基于PSO的优化框架成功地提高了准同轴TSV复合结构的性能.
- 这种方法提高了EM建模的准确性和可靠性,用于RF微系统的设计和开发.
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