Alpha-RF: Automated RF-Filter-Circuit Design with Neural Simulator and Reinforcement Learning

Nhat Tran; Chenjie Hao; Alexander Stameroff; Anh-Vu Pham; Yubei Chen

eess.SPFeb 18, 2026

Alpha-RF: Automated RF-Filter-Circuit Design with Neural Simulator and Reinforcement Learning

Authors: Nhat Tran, Chenjie Hao, Alexander Stameroff, Anh-Vu Pham, Yubei Chen

Organizations: University of California, Davis · Keysight Technologies, Inc.

Abstract

Accurate, high-performance radio-frequency (RF) filter circuits are ubiquitous in radio-frequency communication and sensing systems for accepting and rejecting signals at desired frequencies. Conventional RF filter design process involves manual calculations of design parameters, followed by intuition-guided iterations to achieve the desired response for a set of filter specifications. This process is time-consuming due to time- and resource-intensive electromagnetic simulations using full-wave numerical PDE solvers. This process is also highly sensitive to domain expertise and requires many years of professional training. To address these bottlenecks, we propose an automatic RF filter circuit design tool using neural simulator and reinforcement learning. First, we train a neural simulator to replace the PDE electromagnetic simulator. The neural-network-based simulator reduces each of the simulation time from 4 minutes on average to less than 100 millisecond while maintaining a high precision. Such dramatic acceleration enable us to leverage deep reinforcement learning algorithm and train an amortized inference policy to perform automatic design in the imagined space from the neural simulator. The resulted automatic circuit-design agent achieves super-human design results. The automatic circuit-design agent also reduces the on-average design cycle from days to under a few seconds. Even more surprisingly, we demonstrate that the neural simulator can generalize to design spaces far from the training dataset and in a sense it has learned the underlying physics--Maxwell equations. We also demonstrate that the reinforcement learning has discovered many expert-like design intuitions. This work marks a step in using neural simulators and reinforcement learning in RF circuit design and the proposed method is generally applicable to many other design problems and domains in close affinity

Figures & tables

Figure 1: CNN-based S-Parameters predictor

Figure 2: Workflow of Alpha-RF. During the training phase (left), given target specifications, the agent outputs a complete set of design parameters in one shot. These parameters are passed through the neural simulator to obtain predicted S-parameters, and the reward function converts the mismatch between measurements and target specifications into a scalar reward to update the agent. During the inference phase (right), the trained agent generates multiple candidate designs for target specifications, which are evaluated by the neural simulator. The candidate yielding the highest reward is selected as the final design.

Figure 3: a) Training dynamics of the neural S-parameters simulator b) Theoretical scaling of accuracy

Figure 4: Comparison of predicted S-parameters and true S-parameters

Figure 5: S-Parameters for automatically-generated filter designs of various specifications

Design	$s$	$s_{m}$ (model) $s_{m}$ (true)	$R$ (model) $R$ (true)
(1)	$[35,0.20,-2,-20,-10]$	$[34.35,0.209,-1.92,-23.00,-12.92]$ $[34.25,0.236,-1.39,-28.09,-11.33]$	0.9693 0.8517
(2)	$[31,0.06,-3,-20,-10]$	$[31.45,0.060,-4.18,-26.67,-15.71]$ $[31.15,0.061,-1.89,-23.00,-14.97]$	0.9265 0.9783
(3)	$[31,0.15,-2,-20,-10]$	$[30.95,0.145,-1.94,-17.81,-8.498]$ $[30.75,0.146,-1.27,-17.51,-7.766]$	0.9448 0.9299
(4)	$[31,0.10,-3,-20,-10]$	$[31.25,0.099,-3.60,-22.56,-13.59]$ $[31.15,0.099,-2.13,-19.48,-17.54]$	0.9476 0.9813
(5)	$[33,0.10,-3,-20,-10]$	$[34.10,0.997,-3.41,-17.11,-15.78]$ $[33.55,0.098,-1.63,-21.01,-10.57]$	0.9134 0.9612
(6)	$[33,0.20,-1,-20,-10]$	$[33.35,0.201,-1.82,-29.06,-13.40]$ $[32.80,0.226,-1.34,-24.62,-10.31]$	0.8903 0.8484

Table 1: Specifications, model-predicted and true measurements, and rewards of designs in Figure 5 .

Design	Alpha-RF Reward	Human Reward	Alpha-RF Time (seconds)	Human Time (seconds)
(1)	0.8517	0.7466	7.3	9000
(2)	0.9783	0.9190	7.2	14760
(3)	0.9299	0.9781	7.4	14400
(4)	0.9813	0.8354	7.1	7200
(5)	0.9612	0.9699	7.3	8100
(6)	0.8484	0.9300	7.5	6300

Table 2: Comparison with Human-Designed Filters. Alpha-RF achieves comparable or superior rewards while significantly reducing design time.

Figure 6: Ablation of Test-Time Sampling. Average reward as a function of the sampling budget. The curve shows that Test Time Sampling strategy improves performance as the budget increases.

Figure 7: S-parameters for waveguide-like circuits of different lengths

Figure 8: (a) Designed $l$ for different $f_{0}$ specifications (b) $S_{21}$ (dB) for designed $N$ for different $\alpha_{r},\alpha_{l}$ specifications

Appendix figures & tables3 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 9: (a) Cross-section of the printed circuit board (b) Top-down view of the filter. Light orange circles are vias connecting the top and bottom metal, created by copper-plated drill holes. Rows and columns of vias create the filter structure, specifically the design parameters described in section 2.1

Figure 10: Runtime vs. sampling budget. As the budget grows, runtime increases roughly exponentially, mainly due to CPU-bound image building and batched neural-sim inference on GPU (see Appendix D for details).

Sample Budget	Policy Inference	Build Images	Neural-sim Forward	Interp
1	0.35	0.18	0.27	0.00
10	0.35	0.17	0.26	0.00
50	0.35	0.19	0.24	0.00
100	0.36	0.21	0.25	0.00
200	0.35	0.26	0.27	0.01
500	0.34	0.38	0.31	0.01

Appendix

Table 3: Per-stage evaluation time (seconds) for different sampling budgets. Total time includes policy sampling and all evaluation stages.

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