近日，美国加州大学的Aydogan Ozcan及其研究团队取得一项新进展。经过不懈努力，他们实现线性光学材料衍射信息处理中的非线性编码。相关研究成果已于2024年7月23日在国际知名学术期刊《光：科学与应用》上发表。

光学信息的非线性编码可以通过各种形式的数据表示来实现。该研究团队分析了不同非线性信息编码策略的性能，这些策略可以用于基于线性材料的衍射光学处理器，并揭示了它们与最先进的数字深度神经网络相比的效用和性能差距。为了进行综合评估，研究人员使用不同的数据集来比较包括相位编码等更容易实现的非线性编码策略与基于数据重复的非线性编码策略的统计推理性能。

研究表明，在衍射块内的数据重复(例如，通过光学腔或级联引入输入数据)会导致衍射光学处理器的通用线性变换能力的丧失。因此，基于数据重复的衍射块不能为数字神经网络中常用的全连接层或卷积层提供光学类比。然而，得益于输入信息的非线性编码，它们仍然可以有效地训练特定的推理任务并获得更高的准确性。

这项研究结果还表明，无数据重复输入信息的相位编码提供了一种更简单的非线性编码策略，与基于数据重复的衍射处理器具有相当的统计推理精度。这一分析和结论对于探索基于线性材料的衍射光学系统与视觉信息处理器中非线性编码策略之间的推拉关系具有广泛的意义。

附：英文原文

Title: Nonlinear encoding in diffractive information processing using linear optical materials

Author: Li, Yuhang, Li, Jingxi, Ozcan, Aydogan

Issue&Volume: 2024-07-23

Abstract: Nonlinear encoding of optical information can be achieved using various forms of data representation. Here, we analyze the performances of different nonlinear information encoding strategies that can be employed in diffractive optical processors based on linear materials and shed light on their utility and performance gaps compared to the state-of-the-art digital deep neural networks. For a comprehensive evaluation, we used different datasets to compare the statistical inference performance of simpler-to-implement nonlinear encoding strategies that involve, e.g., phase encoding, against data repetition-based nonlinear encoding strategies. We show that data repetition within a diffractive volume (e.g., through an optical cavity or cascaded introduction of the input data) causes the loss of the universal linear transformation capability of a diffractive optical processor. Therefore, data repetition-based diffractive blocks cannot provide optical analogs to fully connected or convolutional layers commonly employed in digital neural networks. However, they can still be effectively trained for specific inference tasks and achieve enhanced accuracy, benefiting from the nonlinear encoding of the input information. Our results also reveal that phase encoding of input information without data repetition provides a simpler nonlinear encoding strategy with comparable statistical inference accuracy to data repetition-based diffractive processors. Our analyses and conclusions would be of broad interest to explore the push-pull relationship between linear material-based diffractive optical systems and nonlinear encoding strategies in visual information processors.

DOI: 10.1038/s41377-024-01529-8

Source: https://www.nature.com/articles/s41377-024-01529-8