Photonic Reservoir Computing enabled by Active Silicon Micro-Rings

神经形态信号处理(NSP)是近年来出现的一种替代经典信号处理算法和过程的方法。其中，光通信系统可以受益于NSP，因为它具有补偿非线性损伤的能力。NSP没有显式地将处理任务编程到数字信号处理器(DSP)或现场可编程门阵列(FPGA)中，而是采用了一种根本不同的信号处理方法。它使用人工神经网络(ANN)，在那里机器被训练来学习处理任务背后的基本物理模型并采取相应的行动。然而，在高达数百GB/S的所需线速下实现这种用于实时信号处理的最大似然技术是非常具有挑战性的。当信号线速(以及相关带宽)进一步呈指数级增长时，这将在未来变得更加困难。目前可以预见，在中期内，电子电路的信号带宽将被限制在100 GHz到最大几百GHz的范围内。因此，需要将一些信号处理任务转移到光域，在光域中已经可以获得多THz的高得多的带宽。光子库计算(RC)具有作为可扩展硬件实现的能力，这在其他ANN中是独一无二的。在RCS中，只需要(人工神经)网络的输入和输出是自适应的，而不是网络本身。事实上，互联互通被认为是一个“黑匣子”。由于向高维状态的非线性变换是在储油层内部完成的，因此输出成为线性问题。该研究项目的主要目标是分析、制造和演示一种基于硅微环结构的光子库计算机，以补偿光纤传输系统的损伤。为了实现指定的目标，将在商业上可用的铸造厂中设计和制造一种使用CMOS兼容技术来实现蓄水池计算机的硅光电子芯片。为了优化环形腔结构的设计，需要进行密集的数值模拟。制作的芯片将被表征并集成到实验系统测试台中。

Neuromorphic signal processing (NSP) has been emerging in recent years as an alternative to classical signal processing algorithms and processes. Among others, optical communication systems can benefit from NSP due to its ability to compensate nonlinear impairments. Instead of programming the processing tasks explicitly into a digital signal processor (DSP) or field-programmable gate array (FPGA) NSP takes a fundamentally different approach to signal processing. It uses artificial neural networks (ANN), where the machine is trained to learn the basic physical model behind the processing task and to act accordingly. However, it is very challenging to implement such ML techniques for real-time signal processing at the required line rates of up to several hundred Gb/s. This will become even more difficult in the future when signal line rates (and associated bandwidths) further scale exponentially. It can be currently foreseen that the signal bandwidth of electronic circuits will be limited in the range of 100 GHz to a maximum of a few hundred GHz in the medium term. Thus, it is desirable to shift some signal processing tasks to the optical domain, where a much higher bandwidth of multiple THz is available already today.Photonic reservoir computing (RC) has the ability to be implemented as scalable hardware, which is unique among other ANNs. In RCs, only the input and output of the (artificial neural) network need to be adaptive and not the network itself. In fact, the interconnections are considered to be a ‘black box’. Since the nonlinear transformation to a higher dimensional state is done inside the reservoir, the output becomes a linear problem. The primary objective of the proposed research project is to analyze, fabricate and demonstrate a photonic reservoir computer based on a silicon micro-ring structure to compensate for the impairments of a fiber-optic transmission system. To achieve the specified objective, a silicon photonics chip using CMOS compatible technology for the realization of the reservoir computer will be designed and manufactured in a commercially available foundry. Intensive numerical simulations are required to optimize the design of the ring resonator structure. The fabricated chip will be characterized and integrated into an experimental system testbed.