Automated Discovery of Laser Dicing Processes with Bayesian Optimization for Semiconductor Manufacturing
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911292197961728 |
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| author | Leeftink, David Doll, Roman Visserman, Heleen Post, Marco Boughorbel, Faysal Hinne, Max van Gerven, Marcel |
| author_facet | Leeftink, David Doll, Roman Visserman, Heleen Post, Marco Boughorbel, Faysal Hinne, Max van Gerven, Marcel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_23141 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automated Discovery of Laser Dicing Processes with Bayesian Optimization for Semiconductor Manufacturing Leeftink, David Doll, Roman Visserman, Heleen Post, Marco Boughorbel, Faysal Hinne, Max van Gerven, Marcel Machine Learning Systems and Control 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. |
| title | Automated Discovery of Laser Dicing Processes with Bayesian Optimization for Semiconductor Manufacturing |
| topic | Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2511.23141 |