交叉耦合谐振器滤波器的可微综合与良率优化
Differentiable Synthesis and Yield Optimization of Cross-Coupled Resonator Filters
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中文总结 AI 辅助
针对非标准拓扑下交叉耦合谐振器滤波器耦合矩阵综合困难的问题，提出分阶段可微综合框架，以特征多项式匹配为初始目标，显著提升十阶和十六阶基准的成功率，并实现双频带滤波器少一个交叉耦合的设计及良率驱动的设计中心化，将预测良率提高16.1个百分点。
中文摘要 AI 辅助
当期望拓扑为非标准形式时，交叉耦合谐振器滤波器的耦合矩阵综合是困难的。自动微分消除了对特定问题灵敏度表达式的需求，但其本身并不能克服非凸优化景观。本工作开发了一个分阶段的可微综合框架，并通过仅改变第一阶段优化目标的受控比较，确定了其初始目标的重要性。对于十阶和十六阶基准，在指定拓扑下直接匹配频率采样响应，分别从24次和12次随机起点中成功0次和0次。将第一阶段目标替换为特征多项式匹配后，成功率提高到24次中的18次和12次中的12次（精确配对p值分别为7.6×10⁻⁶和4.9×10⁻⁴）。观察到的差异不能由局部条件数解释，且与远离解时有界响应残差的饱和现象一致。一个可微前端将对称多频带规格转换为目标多项式，当端到端应用于一个已制造的双频带滤波器规格时，找到的实现比已发表设计少一个交叉耦合。最后，对蒙特卡洛容差模拟进行微分，实现了以良率驱动的设计中心化，在加性耦合误差模型下，将模型预测的良率从50.9%提高到67.0±0.5%，提高了16.1个百分点。
英文摘要
Coupling-matrix synthesis of cross-coupled resonator filters is difficult when the desired topology is noncanonical. Automatic differentiation removes the need for problem-specific sensitivity expressions, but does not by itself overcome the nonconvex optimization landscape. This work develops a staged differentiable synthesis framework and identifies the importance of its initial objective through a controlled comparison that varies only the objective of the first optimization phase. For tenth- and sixteenth-order benchmarks, directly matching a frequency-sampled response under the prescribed topology reaches the target from $0$ of $24$ and $0$ of $12$ random starts, respectively. Replacing the first-stage objective with characteristic-polynomial matching increases the success rate to $18$ of $24$ and $12$ of $12$ (exact paired $p=7.6\times10^{-6}$ and $4.9\times10^{-4}$). The observed difference is not explained by local conditioning and is consistent with saturation of the bounded response residual far from the solution. A differentiable front end converts symmetric multiband specifications into target polynomials and, when applied end to end to a fabricated dual-band filter specification, finds a realization with one fewer cross-coupling than the published design. Finally, differentiating through Monte Carlo tolerance simulations enables yield-driven design centering, increasing the model-predicted yield from $50.9\%$ to $67.0\pm0.5\%$ under an additive coupling-error model, an improvement of $16.1$ percentage points.
发表机构
- University of California, Irvine（加州大学尔湾分校）
机构由 AI 辅助整理，请以论文原文为准。
