Scalable nanomaterial-based circuits and optoelectronics

基于纳米材料的可扩展电路和光电子学

• 批准号: 2748816
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2748816
• 关键词: Scalable; nanomaterial; based; circuits; optoelectronics

Translating new materials into practical devices of benefit to society typically requires substantial time and capital investment. Devices based on isolated low-dimensional material nanostructures, such as 0D optically active defects, 1D individual nanowires, or 2D heterojunctions, have unique optoelectronic characteristics which can enable electronic and photonic applications not possible with conventional bulk materials. When scaling up these devices for integrated systems, the exact position of each constituent nanostructure needs to be known. As such, fabricating and measuring nanoscale devices, particularly for emerging materials in the early stages, is notoriously labour-intensive. This project will build upon computer-vision-based approaches such as object detection & tracking, and multi-modal data correlation, for high-throughput materials test and evaluation, device routing, and integrated system prototyping. The main objectives are: i) Demonstrate multi-technique, multi-scale characterisation, automatically performed on the same nanostructures between processing steps, to correlate material properties, track processing-induced changes, locate high-quality structures, and address the challenges of yield and scalability. ii) Prototype nanomaterial integrated systems using computer-adjustable electrode designs where a machine-learning algorithm automatically routes individual devices across a chip.

将新材料转化为造福社会的实用装置通常需要大量时间和资本投资。基于孤立的低维材料纳米结构（例如 0D 光学活性缺陷、1D 单个纳米线或 2D 异质结）的器件具有独特的光电特性，可以实现传统块体材料无法实现的电子和光子应用。当将这些设备扩大到集成系统时，需要知道每个组成纳米结构的确切位置。因此，制造和测量纳米级设备，特别是早期阶段的新兴材料，是众所周知的劳动密集型。该项目将建立在基于计算机视觉的方法（例如对象检测和跟踪以及多模式数据关联）的基础上，用于高通量材料测试和评估、设备路由和集成系统原型设计。主要目标是：i) 展示多技术、多尺度表征，在处理步骤之间对相同纳米结构自动执行，以关联材料特性、跟踪处理引起的变化、定位高质量结构，并解决产量和可扩展性的挑战。 ii）使用计算机可调电极设计制作纳米材料集成系统原型，其中机器学习算法自动在芯片上路由各个设备。