Pre-layout design space exploration (DSE) for high-speed signal integrity (SI) analysis is often limited by the computational cost of simulations and iterative optimization algorithms within modern electronic design automation (EDA) workflows. While machine learning surrogate models accelerate the simulation step, optimizing designs still requires utilizing iterative black-box search methods. This iterative nature scales poorly, making multi-corner sweeps computationally expensive. As a solution, this paper proposes amortized neural optimization (ANO) for pre-layout SI design. ANO entirely eliminates iterative black-box inference by utilizing fully differentiable neural network surrogate models. ANO extracts analytical gradients from the surrogate to train a global optimization policy. Instead of solving the optimization problem repeatedly at inference, the optimization process is learned offline and therefore amortized. Once the ANO policy is trained, it maps different channel contexts directly to near-optimal design parameters in a single deterministic forward pass. The efficiency and accuracy of the ANO framework are demonstrated based on three complex SI design scenarios, including DDR5 decision feedback equalization (DFE), 9-dimensional SerDes Tx/Rx co-equalization, and DDR3 DQS differential pair routing to optimize eye diagram metrics under intra-pair skew constraints. By trading roughly 10% in optimality compared to instance-specific black-box algorithms, it realizes speedups of three to four orders of magnitude. For a large-scale 320,000-instance multi-corner SerDes sweep optimization, ANO collapses what would have taken days of computation using iterative search algorithms into a single batched forward pass that completes in milliseconds. This transforms computationally expensive SI optimization into real-time and interactive pre-layout DSE.

翻译：布局前高速信号完整性（SI）分析的设计空间探索（DSE）通常受限于现代电子设计自动化（EDA）工作流程中仿真和迭代优化算法的计算成本。虽然机器学习代理模型加速了仿真步骤，但优化设计仍需依赖迭代式黑箱搜索方法。这种迭代性质导致扩展性差，使得多角点扫描计算成本高昂。作为解决方案，本文提出用于布局前SI设计的摊销神经优化（ANO）。ANO通过利用完全可微的神经网络代理模型，彻底消除了迭代式黑箱推理。ANO从代理模型中提取解析梯度以训练全局优化策略。该优化过程在学习阶段离线完成并实现摊销，而非在推理时反复求解优化问题。训练完成后，ANO策略可通过单次确定性前向传播将不同通道上下文直接映射到近最优设计参数。基于三个复杂SI设计场景（包括DDR5决策反馈均衡（DFE）、9维SerDes发射/接收联合均衡，以及在内对间偏斜约束下优化眼图指标的DDR3 DQS差分对布线），验证了ANO框架的效率与准确性。相比实例特定的黑箱算法，在牺牲约10%最优性的代价下，ANO实现了三到四个数量级的加速。针对大规模32万实例多角点SerDes扫描优化，ANO将从需要迭代搜索算法数天计算量的任务压缩为单次批量前向传播（毫秒级完成）。这将计算昂贵的SI优化转化为实时交互式布局前DSE。