Scalable nanomaterial-based circuits and optoelectronics

基于纳米材料的可扩展电路和光电子学

基本信息

批准号：
2748816
负责人：
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

项目摘要

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.

将新材料转化为对社会利益的实用设备，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的设备，例如0D光学活性缺陷，1D单独的纳米线或2D异质界面具有独特的光电特性，可以通过常规体积材料实现电子和光子应用。在为集成系统扩展这些设备时，需要知道每个组成纳米结构的确切位置。因此，众所周知，制造和测量纳米级设备，尤其是在早期阶段的新兴材料。该项目将基于基于计算机的基于计算机的方法，例如对象检测和跟踪以及多模式数据相关性，用于高通量材料测试和评估，设备路由以及集成的系统原型设计。主要目的是：i）演示多技术，多尺度表征，自动在处理步骤之间的相同纳米结构上执行，以相关材料属性，跟踪处理诱导的变化，定位高质量的结构，并应对产量和可伸缩性的挑战。 ii）使用计算机调整的电极设计的原型纳米材料集成系统，其中机器学习算法自动将单个设备跨越芯片。