The automatic synthesis of analog circuits presents significant challenges. Most existing approaches formulate the problem as a single-objective optimization task, overlooking that design specifications for a given circuit type vary widely across applications. To address this, we introduce specification-conditioned analog circuit generation, a task that directly generates analog circuits based on target specifications. The motivation is to leverage existing well-designed circuits to improve automation in analog circuit design. Specifically, we propose CktGen, a simple yet effective variational autoencoder that maps discretized specifications and circuits into a joint latent space and reconstructs the circuit from that latent vector. Notably, as a single specification may correspond to multiple valid circuits, naively fusing specification information into the generative model does not capture these one-to-many relationships. To address this, we decouple the encoding of circuits and specifications and align their mapped latent space. Then, we employ contrastive training with a filter mask to maximize differences between encoded circuits and specifications. Furthermore, classifier guidance along with latent feature alignment promotes the clustering of circuits sharing the same specification, avoiding model collapse into trivial one-to-one mappings. By canonicalizing the latent space with respect to specifications, we can search for an optimal circuit that meets valid target specifications. We conduct comprehensive experiments on the open circuit benchmark and introduce metrics to evaluate cross-model consistency. Experimental results demonstrate that CktGen achieves substantial improvements over state-of-the-art methods.

翻译：模拟电路的自动综合面临重大挑战。现有方法大多将该问题表述为单目标优化任务，忽视了给定电路类型的设计规范在不同应用间差异显著。为解决这一问题，我们提出规范条件化模拟电路生成任务，即根据目标规范直接生成模拟电路。其核心动机在于利用现有设计优良的电路来提升模拟电路设计的自动化水平。具体而言，我们提出CktGen——一种简洁高效的变分自编码器，将离散化的规范与电路映射至联合隐空间，并从隐向量重构电路。值得注意的是，由于单一规范可能对应多个有效电路，简单地将规范信息融合到生成模型中无法捕捉这种一对多关系。为此，我们解耦电路与规范的编码过程，并对其映射的隐空间进行对齐。随后，采用带滤波掩码的对比训练以最大化编码电路与规范间的差异。此外，结合分类器引导与隐特征对齐的策略，促使共享相同规范的电路形成聚类，避免模型坍缩为平凡的一对一映射。通过对隐空间进行规范化校准，我们能够搜索满足有效目标规范的最优电路。我们在开放电路基准上进行了全面实验，并引入跨模型一致性评估指标。实验结果表明，CktGen相较于现有最优方法取得了显著提升。