Sensor fusion enabled by photonic neuromorphic computing using phase-change materials

使用可以“积累”信息的功能材料来就地执行存储和计算任务是一个正在发展的领域。将光学集成集成到硅芯片上提供了一个独特的机会，将硅缩放和光学波长复用的优势结合到单个平台上。在本提案中，我们将显着扩展我们在执行非冯·诺伊曼计算（例如直接在硬件中进行向量矩阵乘法）方面的初步工作到更大的矩阵，以证明此类硬件在国防相关计算任务中的潜力的实验室规模演示。相变器件，利用非晶态和晶态之间的可逆循环的硫系合金，如Ge2Sb2Te5 (GST)，已经有一段时间了，在光学领域（例如DVD-RAM和蓝光RW磁盘）和最近的电领域（例如英特尔/美光在2015年宣布的3D-XPoint存储器）提供二进制非易失性存储器。与他人一起，我们最近也表明，基于相变的设备可以提供许多重要的非冯·诺伊曼计算/处理功能，即：1)通过利用多级存储操作模式，相变设备可以模拟生物突触的基本操作。2)通过在“能量”积累模式下工作，相变装置可以模拟生物神经元的基本操作。3)通过利用积累模式，相变器件还可以提供一种非冯·诺伊曼计算形式，其中存储和（算术）处理在同一器件中同时进行。在最近的工作中（Li et al ., Optica 7(3), 218-225），我们展示了硅波导和氮化硅波导之间的定量实验差异，并在SOI平台上演示了多位光写入。在这个博士提案中，我们将建立在经过验证的基本原理的坚实基础上，并开发（设计，制造和测试）基于相变的计算原语，特别是突触和神经元模拟以及（二进制和多级）非易失性存储器，并将这些原语组合到非冯·诺伊曼计算网络（架构）中。我们将使用这些来演示与传感器处理和管理相关的国防相关计算任务的概念验证。

The use of functional materials that can "accumulate" information to carry out both memory and computing tasks in-situ is a growing field. Using optical integration onto silicon chips provides a unique opportunity to combine the benefits of silicon scaling and the wavelength multiplexing of optics onto a single platform. In this proposal, we will significantly expand our initial work on carrying out non-von Neumann computations (such as Vector-Matrix Multiplication directly in hardware) to a larger matrix to prove a lab scale demonstration of the potential for such hardware in defence-relevant computing tasks. Phase-change devices, that exploit reversible cycling between amorphous and crystalline states in chalcogenide alloys such as Ge2Sb2Te5 (GST), have been with us for some time now, providing binary non-volatile memories in both the optical domain (e.g. DVD-RAM and Blu-Ray RW disks) and more recently the electrical domain (e.g. in the 3D-XPoint memory announced by Intel/Micron in 2015). Along with others, we have also recently shown that phase-change based devices can provide a number of important non-von Neumann computing/processing functionalities, namely: 1) By exploiting a multi-level memory mode of operation, phase-change devices can mimic the basic operation of a biological synapse.2) By operating in an 'energy' accumulation mode, phase-change devices can mimic the basic operation of a biological neuron.3) By exploiting the accumulation mode, phase-change devices can also provide a form of non-von Neumann computing in which memory and (arithmetic) processing are carried out simultaneously in the same device. In recent work (Li et al, Optica 7 (3), 218-225), we have shown the quantitative experimental differences between Silicon and Silicon Nitride waveguides, and demonstrated multibit optical writing on a SOI platform. In this PhD proposal we will build on this firm foundation of proven basic principles and develop (design, fabricate and test) phase-change-based computing primitives, specifically synapse and neuron mimics and (binary and multi-level) non-volatile memories and combine such primitives into non-von Neumann computing networks (architectures). We will use these to demonstrate proof-of-concept for defence-relevant computational tasks related to sensor processing and management.