Development of New Materials for Optically Programmable and Re-Programmable Printable Memristors for Neuromorphic Computing

用于神经形态计算的光学可编程和可重新编程可打印忆阻器新材料的开发

• 批准号: 2659392
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2659392
• 关键词: Development; New; Materials; Optically; Programmable

Printed electronics (i.e. the technology of fabricating electronic devices and circuits using additive, low-temperature printing processes) promises low-cost, on-demand electronic circuit fabrication, in new form-factors (e.g. on flexible or conformal substrates, using biocompatible components, over large areas). The manufacture of printed electronic systems requires much simpler and cheaper hardware solutions than those needed for making traditional integrated circuits and some of the most promising commercial opportunities in the Internet of Things are in intelligent sensors, smart tags and labels, and other 'edge computing' scenarios, where intelligent processing is embedded in the low-cost device itself not transferred to the cloud. Delivery of these systems requires the low-power, low-cost, and low component count of analogue computing, combined with deep learning algorithms and this has led to a resurgence of interest in analogue neural network computing, sometimes termed a "neuromorphic" approach to computation. Critical components of the hardware needed to implement these machine learning algorithms require new materials for robust, programmable, printed devices. In this PhD project new materials and formulations will be developed to deliver arrays and circuits in which the materials resistance is capable of being programmed after fabrication.This will be initially achieved by optical encoding of materials resistance to decouple the write and read processes in memristor devices to deliver stable, robust and reproducible programmable arrays and hence circuits (e.g. for the synaptic weights of neural networks). This approach will be extended to develop materials for optically re-programmable arrays in which illumination at different wavelengths will be used to repeatedly encode and erase the resistance of device arrays. Finally the research programme will investigate approaches to move from 2D planar printed arrays to 3D folded or printed structures with a greater device density and a more highly interconnected circuit architecture such as that seen in the human cortex.

印刷电子（即使用添加剂、低温印刷工艺制造电子器件和电路的技术）承诺以新的形状因子（例如，在柔性或共形衬底上，使用生物相容性组件，在大面积上）低成本、按需电子电路制造。印刷电子系统的制造需要比制造传统集成电路所需的硬件解决方案更简单，更便宜的硬件解决方案，物联网中一些最有前途的商业机会是智能传感器，智能标签和标签以及其他“边缘计算”场景，其中智能处理嵌入在低成本设备本身中，而不是转移到云端。这些系统的交付需要模拟计算的低功耗，低成本和低组件数量，结合深度学习算法，这导致了对模拟神经网络计算的兴趣重新抬头，有时被称为“神经形态”计算方法。实现这些机器学习算法所需的硬件的关键组件需要新材料来制造强大的可编程印刷设备。在这个博士项目中，将开发新的材料和配方，以提供阵列和电路，其中材料电阻能够在制造后进行编程。这将首先通过材料电阻的光学编码来实现，以解耦忆阻器件中的写入和读取过程，从而提供稳定，鲁棒和可重复的可编程阵列和电路（例如神经网络的突触权重）。这种方法将被扩展到开发用于光学可重新编程阵列的材料，其中不同波长的照明将用于重复编码和擦除器件阵列的电阻。最后，该研究计划将研究从2D平面打印阵列转移到3D折叠或打印结构的方法，这些结构具有更大的设备密度和更高度互连的电路架构，例如在人类皮层中看到的结构。