Scalable nanomaterial-based circuits and optoelectronics

将新材料转化为有益于社会的实用设备通常需要大量的时间和资本投资。基于孤立的低维材料纳米结构的器件，例如0D光学活性缺陷、1D单个纳米线或2D异质结，具有独特的光电特性，可以实现传统块状材料不可能实现的电子和光子应用。当将这些器件放大到集成系统时，需要知道每个组成纳米结构的确切位置。因此，制造和测量纳米级器件，特别是在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基础上，如物体检测和跟踪，多模态数据相关，用于高通量材料测试和评估，设备路由和集成系统原型。主要目标是：i）展示多技术、多尺度表征，在加工步骤之间对相同的纳米结构自动执行，以关联材料特性，跟踪加工引起的变化，定位高质量结构，并解决产量和可扩展性的挑战。ii）使用计算机可调节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在芯片上路由各个设备。

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.