With the swift advancement of neural networks and their expanding applications in many fields, optical neural networks have gradually become a feasible alternative to electrical neural networks due to their parallelism, high speed, low latency, and power consumption. Nonetheless, optical nonlinearity is hard to realize in free-space optics, which restricts the potential of the architecture. To harness the benefits of optical parallelism while ensuring compatibility with natural light scenes, it becomes essential to implement two-dimensional spatial nonlinearity within an incoherent light environment. Here, we demonstrate a lensless opto-electrical neural network that incorporates optical nonlinearity, capable of performing convolution calculations and achieving nonlinear activation via a quantum dot film, all without an external power supply. Through simulation and experiments, the proposed nonlinear system can enhance the accuracy of image classification tasks, yielding a maximum improvement of 5.88% over linear models. The scheme shows a facile implementation of passive incoherent two-dimensional nonlinearities, paving the way for the applications of multilayer incoherent optical neural networks in the future.

中文翻译：

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具有量子点非线性激活的无透镜光电神经网络

随着神经网络的快速发展及其在许多领域的应用不断扩大，光神经网络由于其并行性、高速度、低延迟和功耗等优点逐渐成为电神经网络的可行替代方案。尽管如此，光学非线性在自由空间光学中很难实现，这限制了该架构的潜力。为了利用光学并行性的优势，同时确保与自然光场景的兼容性，在非相干光环境中实现二维空间非线性变得至关重要。在这里，我们演示了一种无透镜光电神经网络，它结合了光学非线性，能够执行卷积计算并通过量子点薄膜实现非线性激活，所有这些都无需外部电源。通过仿真和实验，所提出的非线性系统可以提高图像分类任务的准确性，比线性模型最大提高5.88%。该方案展示了无源非相干二维非线性的简便实现，为未来多层非相干光神经网络的应用铺平了道路。