Photonic Reservoir Computing enabled by Active Silicon Micro-Rings

由活性硅微环实现的光子储层计算

• 批准号: 498410117
• 负责人: Professor Dr. Kambiz Jamshidi, Ph.D.
• 金额: --
• 依托单位: Professur für Hochfrequenztechnik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/498410117?language=en
• 关键词: Photonic; Reservoir; Computing; enabled; Active

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.

近年来，神经形态信号处理 (NSP) 逐渐兴起，作为经典信号处理算法和过程的替代方案。其中，光通信系统可以从 NSP 中受益，因为它能够补偿非线性损伤。 NSP 没有将处理任务显式编程到数字信号处理器 (DSP) 或现场可编程门阵列 (FPGA) 中，而是采用了根本不同的信号处理方法。它使用人工神经网络（ANN），机器经过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，以高达数百 Gb/s 的线速率实现实时信号处理的机器学习技术非常具有挑战性。当信号线速率（和相关带宽）进一步呈指数级增长时，这在未来将变得更加困难。目前可以预见，中期来看电子电路的信号带宽将被限制在100GHz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学领域，目前光学领域已经可以使用更高的多个太赫兹带宽。光子储层计算（RC）具有作为可扩展硬件实现的能力，这在其他人工神经网络中是独一无二的。在 RC 中，只有（人工神经）网络的输入和输出需要自适应，而不是网络本身。事实上，互连被认为是一个“黑匣子”。由于向高维状态的非线性变换是在储层内部完成的，因此输出变成了线性问题。该研究项目的主要目标是分析、制造和演示基于硅微环结构的光子储存计算机，以补偿光纤传输系统的缺陷。为了实现指定目标，将在商业代工厂中设计和制造采用CMOS兼容技术的硅光子芯片，以实现储层计算机。需要进行深入的数值模拟来优化环形谐振器结构的设计。制造的芯片将被表征并集成到实验系统测试台中。