用神经仿真器和强化学习自动设计射频滤波电路，效率提升百倍。
Alpha-RF: Automated RF-Filter-Circuit Design with Neural Simulator and Reinforcement Learning
- 用神经网络替代传统电磁仿真，单次仿真时间从4分钟降至100毫秒。
- 自动化设计工具将平均设计周期从数天缩短至几秒内。
- 模型能泛化到未训练区域，隐含学习了麦克斯韦方程物理规律。
高性能射频（RF）滤波电路广泛应用于无线通信与传感系统中，用于选择或抑制特定频率信号。传统设计依赖人工计算参数并结合经验迭代，过程耗时且高度依赖专业经验，需使用全波数值偏微分方程（PDE）求解器进行电磁仿真，耗时耗资源。为此，本文提出一种基于神经仿真器与强化学习的自动射频滤波电路设计方法。首先，训练一个神经网络仿真器替代传统PDE电磁仿真器，将单次仿真时间由平均4分钟降至不足100毫秒，同时保持高精度。该加速使深度强化学习算法可在神经仿真器生成的虚拟空间中训练出可泛化的推理策略，实现自动化电路设计。所提出的自动设计代理达成超人类设计性能，平均设计周期从数天缩短至数秒以内。更令人惊讶的是，该神经仿真器在远离训练数据的设计空间仍具泛化能力，表明其已隐式学习到底层物理规律——麦克斯韦方程组。此外，强化学习还发现了诸多专家级设计直觉。本工作标志着神经仿真器与强化学习在射频电路设计中的重要进展，方法具有广泛适用性，可推广至其他类似设计领域。
原文摘要 · Abstract (English)
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
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