Since the late 16th century, scientists have continuously innovated and developed new microscope types for various applications. Creating a new architecture from the ground up requires substantial scientific expertise and creativity, often spanning years or even decades. In this study, we propose an alternative approach called "Differentiable Microscopy," which introduces a top-down design paradigm for optical microscopes. Using all-optical phase retrieval as an illustrative example, we demonstrate the effectiveness of data-driven microscopy design through $\partial\mu$. Furthermore, we conduct comprehensive comparisons with competing methods, showcasing the consistent superiority of our learned designs across multiple datasets, including biological samples. To substantiate our ideas, we experimentally validate the functionality of one of the learned designs, providing a proof of concept. The proposed differentiable microscopy framework supplements the creative process of designing new optical systems and would perhaps lead to unconventional but better optical designs.

翻译：自16世纪末以来，科学家不断创新并为各种应用开发新型显微镜。从零开始构建一种新的架构需要大量的科学专业知识和创造力，通常需要数年甚至数十年的时间。在本研究中，我们提出了一种名为“可微分显微镜”（Differentiable Microscopy）的替代方法，它引入了一种自顶向下设计光学显微镜的范式。以全光学相位恢复（all-optical phase retrieval）为例，我们通过 $\partial\mu$ 展示了数据驱动显微镜设计的有效性。此外，我们与竞争方法进行了全面比较，展示了所学设计在包括生物样本在内的多个数据集上的一致优越性。为了验证我们的想法，我们通过实验验证了其中一个学习设计的功能，提供了概念验证。所提出的可微分显微镜框架补充了设计新型光学系统的创造性过程，并可能引领非传统但更优的光学设计。