Presentation
Perifi:physics-Infused RF Inverse Design with Parametric Data-Efficient Feasible-Region Sampling
DescriptionRecent AI-driven inverse design approaches have shown promise
in synthesizing complex electromagnetic (EM) structures for radio-
frequency (RF) circuits. However, existing methods suffer from two
fundamental limitations: random topology generation often pro-
duces physically infeasible layouts, and pixel-based representations
encode only topology while ignoring geometric dimensions, forc-
ing complete dataset regeneration and model retraining whenever
layout scale changes. To address these issues, we propose PeRIFi,
a physics-constrained and geometry-aware inverse design frame-
work built on three key innovations. (1) Feasibility-aware param-
eterization integrates B-splines, which guarantee direct-current
(DC) connectivity, with level-set representations that enable flexible
geometric variation, ensuring 100% physically feasible structure
generation. (2) Explicit geometric encoding decouples topol-
ogy from geometric dimensions, allowing a single surrogate model
to generalize across multiple layout scales without regenerating
datasets. (3) High-dimensional optimization employs Particle
Swarm Optimization tailored to the proposed 268-dimensional fea-
sible design space. Experimental results demonstrate substantial
data-efficiency gains: with only 5k training samples, PeRIFi attains
73% lower MSE and a 6.7% higher 𝑅^2 than a pixel-based baseline
trained on 20k samples. Furthermore, PeRIFi reduces optimization
cost by 22.19%–34.71% and lowers prediction error (MAE) by 58.2%
compared with state-of-the-art methods, enabling more accurate
and scalable RF inverse design.
in synthesizing complex electromagnetic (EM) structures for radio-
frequency (RF) circuits. However, existing methods suffer from two
fundamental limitations: random topology generation often pro-
duces physically infeasible layouts, and pixel-based representations
encode only topology while ignoring geometric dimensions, forc-
ing complete dataset regeneration and model retraining whenever
layout scale changes. To address these issues, we propose PeRIFi,
a physics-constrained and geometry-aware inverse design frame-
work built on three key innovations. (1) Feasibility-aware param-
eterization integrates B-splines, which guarantee direct-current
(DC) connectivity, with level-set representations that enable flexible
geometric variation, ensuring 100% physically feasible structure
generation. (2) Explicit geometric encoding decouples topol-
ogy from geometric dimensions, allowing a single surrogate model
to generalize across multiple layout scales without regenerating
datasets. (3) High-dimensional optimization employs Particle
Swarm Optimization tailored to the proposed 268-dimensional fea-
sible design space. Experimental results demonstrate substantial
data-efficiency gains: with only 5k training samples, PeRIFi attains
73% lower MSE and a 6.7% higher 𝑅^2 than a pixel-based baseline
trained on 20k samples. Furthermore, PeRIFi reduces optimization
cost by 22.19%–34.71% and lowers prediction error (MAE) by 58.2%
compared with state-of-the-art methods, enabling more accurate
and scalable RF inverse design.
Event Type
Research Manuscript
TimeWednesday, July 293:54pm - 4:06pm PDT
LocationMtg Room 101B
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