Photonic-based reservoir computing (RC) systems have attracted significant attention. Integrated and purely passive systems are compatible with complementary metal-oxide-semiconductor devices, but are limited by the lack of non-linear components. This study consists of two parts: firstly, a review on the published integrated and passive RC system is presented. The review focuses on the structural configuration (rather than the mathematical model) of the neural network; secondly, a new approach for achieving an integrated and passive photonic RC system is introduced and discussed. This approach employs a mode combiner in front of the reservoir to achieve an extra non-linearity in a purely passive device. Moreover, the approach is numerically investigated, and an XOR (exclusive or) task is used to test the device, and the result shows that the new approach satisfies the requirement of an RC system.

基于光子的储层计算（RC）系统已引起广泛关注。集成的纯无源系统与互补的金属氧化物半导体器件兼容，但由于缺少非线性组件而受到限制。这项研究包括两个部分：首先，对已发布的集成式和被动式RC系统进行了综述。本文的重点是神经网络的结构配置（而不是数学模型）。其次，介绍并讨论了一种实现集成和无源光子RC系统的新方法。这种方法在储液罐前采用了模式组合器，以在纯无源设备中实现额外的非线性。此外，对该方法进行了数值研究，并使用XOR（异或）任务来测试设备，