模拟电路自动综合仍是一项极具挑战性的任务。现有多数方法将其建模为单目标优化问题，却忽视了同一类电路在不同应用场景下往往对应差异显著的设计规格。为克服这一局限，本文提出以规格为条件的模拟电路生成任务，旨在依据给定规格直接生成相应的模拟电路。其核心出发点在于充分利用已有的高质量电路设计数据，提升模拟电路设计的自动化水平。为此，本文提出简单而有效的变分自编码器模型CktGen，将离散化后的规格与电路共同映射到联合潜空间，并据此重构电路。由于单一规格通常对应多种有效电路，若将规格信息直接融入生成模型，往往难以刻画这种一对多映射关系。为解决这一问题，本文首先解耦电路与规格的编码过程，并对两者的潜在表示进行对齐；随后引入带过滤掩码的对比训练，以增强不同样本对之间的表征可分性；进一步结合分类器引导和潜在特征对齐，促使相同规格对应的电路在潜空间中形成有效聚类，从而避免模型坍塌为简单的一对一映射。基于规格对潜空间进行规范化后，模型还能够进一步搜索并优化满足目标规格的最优电路。本文在 Open Circuit Benchmark 上开展了系统实验，并引入若干指标，用于评估以规格为条件的电路生成任务中的跨模态一致性。实验结果表明，与现有最先进方法相比，CktGen 在多项任务上均取得了显著性能提升。

The automatic synthesis of analog circuits presents significant challenges. Most existing approaches formulate the problem as a single-objective optimization task, overlooking the fact that design specifications for a given circuit type can vary widely across applications. To address this limitation, we introduce specification-conditioned analog circuit generation, a task that directly generates analog circuits based on stated specifications. The motivation is to find an effective method that leverages existing well-designed circuits to improve automation in analog circuit design. Specifically, we propose CktGen, a simple yet effective variational autoencoder model 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 the specification information into a generative model does not capture these one-to-many relationships. To address this, we first decouple the encoding process 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, thus avoiding model collapse into trivial one-to-one mappings. By canonicalizing the latent space with respect to the specifications, we can further optimize and search for an optimal circuit that meets the valid target specification. We conduct comprehensive experiments on the open circuit benchmark and introduce several metrics to evaluate cross-model consistency in the specification-conditioned circuit generation task. The experimental results demonstrate that CktGen achieves substantial improvements over existing state-of-the-art methods.