Automated Discovery of Laser Dicing Processes with Bayesian Optimization for Semiconductor Manufacturing
This work addresses the time-consuming adaptation of laser dicing to new wafer materials in semiconductor manufacturing, offering an incremental improvement by automating a process that previously relied heavily on manual expert input.
The researchers tackled the problem of automating laser dicing processes for semiconductor manufacturing, which traditionally requires weeks of expert effort, by using Bayesian optimization to autonomously discover configurations that match or exceed expert baselines in production speed, die strength, and structural integrity on silicon and product wafers.
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.