概括
这项研究引入了一种新的基于物理的深度学习管道,用于光学制造中的主动对齐 (AA). 该方法显著提高了对应速度和准确性,用于复杂的系统,如智能手机镜头.
科学领域:
- 光学工程是指光学工程.
- 机器学习 机器学习
- 制造业 制造技术 制造技术
背景情况:
- 复杂的光学系统需要精确的对齐以获得最佳性能.
- 当前的主动对齐 (AA) 方法在大规模制造中在平衡速度和精度方面面临挑战.
- 高效的对齐对于大量生产至关重要,例如在智能手机镜头制造中.
研究的目的:
- 开发一种使用物理信息的深度学习的新型主动对齐管道.
- 在大规模制造中提高光学对齐的速度和准确性.
- 解决现有的AA技术在平衡吞吐量和精度方面的局限性.
主要方法:
- 提出了一个由两个组件组成的管道:一个基于物理的耐受性估计神经网络 (TolNet) 和一个光学优化模块.
- 托尔网使用混合数据驱动和物理驱动的损失策略从点差函数 (PSF) 估计公差.
- 光学优化模块确定AA的调整参数.
主要成果:
- 拟议的基于物理的深度学习管道实现了对主动对齐的异常速度.
- 托尔网在0.01秒以下完成了耐受性估计.
- 光学优化模块需要不到3秒的时间来确定调节参数.
- 实验验证证了该方法在提高AA效率和精度方面的有效性.
结论:
- 开发的基于物理的深度学习管道为复杂光学系统中的主动对齐提供了重大进展.
- 该方法为提高大规模制造环境中的效率和精度提供了有希望的解决方案.
- 这种方法有效地平衡了现代光学生产的速度和精度要求.
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