Unlocking NANOtechnology through autoMATION

将新材料转化为对社会有益的实用设备的过程通常需要大量的时间和资本投入。由于其独特的几何形状和材料特性，基于纳米材料结构的器件具有独特的（光电）电子特性，使传统块状材料无法实现应用。当基于单个纳米结构创建设备时，需要知道该结构的确切位置。制造和测量纳米级器件是出了名的劳动密集型，包括在手动布线电极布局之前进行搜索和校准，或者手动执行拾取和放置将这些纳米结构转移到现有的电极配置上。在研究环境中，这种对人为干预的需求是减缓新纳米材料技术发展的一个重要瓶颈。更糟糕的是，这种方法的缓慢吞吐量阻碍了它在任何制造环境中的应用。我们已经开发了一种三管齐下的方法——统称为NanoMation——以消除检查、研究和制造过程中所需的人为干预。第一个是基准标记系统，“光刻标签”，它是光刻工艺的优化-光刻，电子束，或纳米压印。这些标记可以很容易地通过自动显微镜过程读取。第二个是计算机视觉系统，它可以根据需要的性质找到、分类和过滤纳米结构。第三种是计算机可调电极设计系统，其中机器学习算法自动布线支持电极形成整个电路。这些工艺将实现从单个原型器件到高性能集成系统的快速过渡（例如，单单元纳米材料光电探测器、晶体管或led分别到图像传感器、集成电路和显示器）。

The process of translating new materials into practical devices of benefit to society typically requires substantial time and capital investment. By virtue of their unique geometries and material properties, devices based on nanomaterial structures have unique (opto)electronic characteristics enabling applications not possible with conventional bulk materials. When creating a device based on an individual nanostructure, that structure's exact position needs to be known. Fabricating and measuring nanoscale devices is notoriously labour-intensive, involving searching and alignment before manual routing of electrode layout, or manually performing pick-and-place to transfer these nanostructures onto existing electrode configurations. In a research setting, this need for human intervention is a significant bottleneck that slows the development of new nanomaterials-enabled technologies. Worse still, the slow throughput of this approach precludes its application in any manufacturing setting.We have developed a three-pronged approach - together known as NanoMation - to remove the human intervention required during inspection, research and manufacturing. The first is a system of fiducial markers, "LithoTags", which are optimised for lithography processing - photo-, electron beam-, or nanoimprint lithography. These markers can be easily read by automated microscopy processes. The second is a computer-vision system that can find, sort and filter nanostructures depending on desired properties. Third is a system of computer-adjustable electrode designs where a machine-learning algorithm automatically routes the supporting electrodes to form an entire circuit. These processes will enable a rapid transition from individual prototype devices to high performance integrated systems (e.g. single-unit nanomaterial photodetectors, transistors, or LEDs respectively - to image sensors, integrated circuits, and displays).