首个可微分的离散MicroLED修复算法，大幅减少转移步骤。
The First Differentiable Transfer-Based Algorithm for Discrete MicroLED Repair
- 设计可微分转移模块，实现梯度优化的离散位移规划。
- 2000×2000阵列上减少50%转移步数，规划时间不足2分钟。
- 无需手工特征提取，适合大规模AR/VR显示制造。
激光辅助的选择性转移是高通量MicroLED制造中的关键环节，需要计算模型来规划位移序列，以最小化XY平台运动并适应基板上不同的优化目标。本文提出首个基于可微分转移模块的修复算法，能够建模转移平台的离散位移，同时支持梯度优化训练。相比局部邻近搜索算法，本方法在修复性能上表现更优，并可灵活设计目标，如最小化步骤数。与强化学习方法不同，该方法无需手工特征提取，训练速度显著提升，具备向大规模阵列扩展的能力。实验表明，在2000×2000阵列上，转移步骤减少50%，规划时间低于2分钟。该方法为AR/VR及下一代显示制造中的MicroLED修复提供了高效且可拓展的解决方案。
原文摘要 · Abstract (English)
Laser-enabled selective transfer, a key process in high-throughput microLED fabrication, requires computational models that can plan shift sequences to minimize motion of XY stages and adapt to varying optimization objectives across the substrate. We propose the first repair algorithm based on a differentiable transfer module designed to model discrete shifts of transfer platforms, while remaining trainable via gradient-based optimization. Compared to local proximity searching algorithms, our approach achieves superior repair performance and enables more flexible objective designs, such as minimizing the number of steps. Unlike reinforcement learning (RL)-based approaches, our method eliminates the need for handcrafted feature extractors and trains significantly faster, allowing scalability to large arrays. Experiments show a 50% reduction in transfer steps and sub-2-minute planning time on 2000x2000 arrays. This method provides a practical and adaptable solution for accelerating microLED repair in AR/VR and next-generation display fabrication.
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