用贝叶斯优化自动发现半导体激光切割工艺，省时高效。
Automated Discovery of Laser Dicing Processes with Bayesian Optimization for Semiconductor Manufacturing
- 将激光切割问题建模为高维多目标贝叶斯优化，分两阶段降低实验成本。
- 在裸硅片和成品晶圆上实现速度、强度与完整性均达或超过人工基准。
- 可生成多种权衡方案，适合需要快速适配新材料的工业场景。
半导体晶圆的激光切割是微电子制造中的关键步骤，需通过多次顺序激光加工精确分离单个芯片。针对新晶圆材料，传统方法需专家耗时数周来平衡加工速度、切割质量和材料完整性。本文首次在工业LASER1205切割系统上实现生产级激光切割工艺的自动化发现。将问题建模为高维、带约束的多目标贝叶斯优化任务，并引入分层双精度策略，显著减少昂贵的破坏性芯片强度测试次数。在裸硅片和成品晶圆上，该方法仅需技术人员操作即可自动获得可行配置，其生产速度、芯片强度及结构完整性均达到或优于人工基准。事后验证不同效用函数权重组合表明，最终代理模型可生成多种具有不同权衡特性的可行解。专家对发现结果进行微调后，进一步提升生产速度，同时保持芯片强度与结构完整性，超越纯人工或纯自动化方法。
原文摘要 · Abstract (English)
Laser dicing of semiconductor wafers is a critical step in microelectronic manufacturing, where multiple sequential laser passes precisely separate individual dies from the wafer. Adapting this complex sequential process to new wafer materials typically requires weeks of expert effort to balance process speed, separation quality, and material integrity. We present the first automated discovery of production-ready laser dicing processes on an industrial LASER1205 dicing system. We formulate the problem as a high-dimensional, constrained multi-objective Bayesian optimization task, and introduce a sequential two-level fidelity strategy to minimize expensive destructive die-strength evaluations. On bare silicon and product wafers, our method autonomously delivers feasible configurations that match or exceed expert baselines in production speed, die strength, and structural integrity, using only technician-level operation. Post-hoc validation of different weight configurations of the utility functions reveals that multiple feasible solutions with qualitatively different trade-offs can be obtained from the final surrogate model. Expert-refinement of the discovered process can further improve production speed while preserving die strength and structural integrity, surpassing purely manual or automated methods.
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