AI 中文总结
研究开源EDA工具中设计空间探索问题，提出受保护白盒DSE框架ReviewDSE，通过构建证据、初始化探索等操作，在OpenROAD详细布局中应用，有效降低线长，还能揭示故障并修复，提升设计空间探索效果。
AI 中文摘要
开源EDA工具使设计空间探索（DSE）超越公共旋钮，进入分阶段优化器内有限的源级机制。我们提出了ReviewDSE，这是一个受保护的白盒DSE框架，用于探索目标设计的此类机制。ReviewDSE在受保护的评估器下评估完整的源候选，并将可重用的搜索知识记录为经过审查的机制级证据。它首先从校准设计构建方法证据和源起始分支，然后使用这些固定的热启动产物在教师审查和全流程验证下初始化目标案例探索。我们将ReviewDSE实例化为OpenROAD详细布局，作为一个有代表性的分阶段开源EDA优化器。在九个目标任务中，与公共旋钮黑盒DSE的0.38%相比，ReviewDSE在2倍运行时门限下平均将最终的DPL后半周长线长（HPWL）降低了1.78%。运行时感知的ReviewDSE选择在1.11倍运行时保留了1.68%的降低，全流程审查揭示了阶段可组合性故障，而源机制探索修复了硬割行合法性故障。
英文摘要
Open-source EDA tools allow design-space exploration (DSE) to move beyond public knobs and into bounded source-level mechanisms inside staged optimizers. We present ReviewDSE, a protected white-box DSE framework that explores such mechanisms for a target design. ReviewDSE evaluates complete source candidates under a protected evaluator and records reusable search knowledge as reviewed mechanism-level evidence. It first constructs method evidence and source-start branches from calibration designs, then uses these fixed warm-start products to initialize target-case exploration under Teacher review and full-flow validation. We instantiate ReviewDSE on OpenROAD detailed placement as a representative staged open-source EDA optimizer. Across nine target tasks, ReviewDSE reduces final post-DPL half-perimeter wirelength (HPWL) by 1.78\% on average under a 2$\times$ runtime gate, compared with 0.38\% for public-knob black-box DSE. A runtime-aware ReviewDSE selection retains a 1.68\% reduction at 1.11$\times$ runtime, and full-flow review exposes stage-composability failures while source-mechanism exploration repairs hard cut-row legality failures.
