In the field of electron microscopy, 4D STEM is becoming increasingly popular. In this article, the subject of this fascinating family of techniques is explored in more detail.
图片来源：Lawrence Berkeley National Laboratory’s National Center for Electron Microscopy (NCEM)
Dr. Colin Ophus works at Lawrence Berkeley National Laboratory’s National Center for Electron Microscopy (NCEM), which is part of the Molecular Foundry.
Dr. Ophus originally wanted to be an engineer, so he began his studies in electrical engineering and later switched to engineering physics, with no idea that his professional life would take such an interesting turn.
It was actually an end-of-semester project that was part of his studies which led to a summer internship at the professor’s laboratory, where he became interested in scientific research. He enjoyed having so much freedom in solving problems in science and decided to pursue a Ph.D. in Materials Engineering at that research facility.
当时，实验室在NCEM有联系，而TEM（传输电子显微镜）已经在材料科学研究中获得了吸引力。亚博网站下载亚博老虎机网登录Ophus博士在提交用户建议后，经常一次去加利福尼亚几个月来使用NCEM的显微镜。在那里，VelimirRadmilović训练了Ophus博士，教他所有有关电子显微镜的知识。
Eventually, after his postdoc at Berkeley, Dr. Ophus was offered a position at the NCEM as a computational scientist. From there, things progressed, and he ultimately became a staff scientist with his own computational microscopy group. Dr. Ophus is very pleased with how things turned out. The facility is one of the few in the world that conducts research in a specific manner.
Escape Room, Ophus Style
When it comes to recognizing Dr. Ophus' achievements, it is difficult to choose just one accomplishment. He believes this is because he was fortunate enough to work with so many incredible collaborators. His personal highlight is the PRISM algorithm for STEM (Scanning Transmission Electron Microscopy) simulation—it is the only single-author publication he ever had.
Dr. Ophus had trouble simulating a large field-of-view STEM image because it required over a million separate simulations. So, he locked himself in a room with a pen and paper to come up with a better solution. After a few days, he came up with a few suggestions as to how to improve the process.
After implementing the code, he was able to run STEM image simulations a thousand times faster than ever before. The idea was picked up and implemented in the software of other researchers such as Hamish Brown (from the University of Melbourne) and Thomas Susi and Jacob Madsen (from the University of Vienna). This was evidence that his algorithm was effective.
最近的TEM趋势
When it comes to recent STEM trends, Dr. Ophus may be prejudiced as he is a computational scientist with a data weakness. Nonetheless, he believes that 4D STEM has the most potential for improving data acquisition and data quality.
直接电子探测器的量子效率提高到可以快速计算单个电子以便以其天然速度进行茎实验的点。这些最近的硬件进步，尤其是在探测器技术中，使研究人员能够收集更多更好的数据。
Five Beamlines in One Instrument
Multimodal experiments are another trend that is fascinating. Nowadays, only a few channels are collected: the forward diffraction pattern, a spectroscopic scan of inelastic energy losses, scattered X-Rays, backscattered electrons, or other channels like cathodoluminescence. STEMs, on the other hand, are idealmultimodal instrumentsbecause they can gather all of these data streams at once.
It is the equivalent of five synchrotron beamlines combined into one instrument. These multimodal experiments will yield a statistically significant number of diffraction images or spectra, as well as provide enough data for computational models to produce reliable results.
It is exciting to see how the scientific community is shifting away from the old practice of recording one or two publication-worthy images and instead of recognizing the value of assembling large data sets to gain a better understanding of the big picture. Even more so now, as computer and detector technology advances year after year.
探测器在STEM发展中的作用
A4D STEM detectorshould provide a complete picture of beam-specimen interaction. The ultimate goal in any field, whether it is Life Science or Materials Science, is to gather every electron while maintaining good time resolution.
Scientists in particle physics have been gathering all of the scattered, generated, or annihilated particle trajectories for years to reconstruct what occurred inside the material. Fortunately, the technology to track appropriate physical processes by capturing the position and momentum of all scattered electrons with considerable speed is already present.
Then, of course, a detector can be customized to meet the needs of a specific experiment. There are products available that allow balancing various aspects, like time for spatial resolution or number of pixels versus time resolution.
With the detector development in NCEM, the team is aiming for 100,000 frames per second. Individual electron collection should be simple at this speed, but speed should not come at the cost of a high dynamic range or noise-free readout. Commercial detectors are getting close to the point where it is possible to catch each electron across the entire scattering range in the timeframes that matter.
一个统治所有人的平台
根据Ophus博士的说法，改进的检测器集成到TEM硬件和软件中：无疑需要一个集中的开放控制平台。例如，在4D茎中，需要将检测器与茎线圈和束主梁同步。由于检测器与显微镜的镜头，样品阶段或其他硬件的其余部分无法通信，因此无法进行许多实验。改进4D茎和其他技术的最大障碍是TEM中缺乏集成和开源软件。
MBED AU纳米颗粒的3D断层扫描。yabo214来自所有探针位置的最大CBED图像，虚拟检测器范围覆盖。图片来源：NCEM Colin Ophus博士
The Most Crucial Detector Parameters in 4D STEM
The speed of the detector is one of the most important factor in 4D STEM. Fast detectors can count more individual electrons. Such detectors not only improve quantum efficiency but also allow experiments to run at native speed.
Detectors have evolved over time, from CCD detectors that can capture 10 frames per second to the first generation of direct electron detectors that can capture 1,000 frames per second and now the second generation that can capture 10,000 frames per second.
该技术决定了下一个最重要的参数。一方面，这将是用于衍射实验的像素数量，例如相位定向，图案分类，应变映射以及其他取决于梁的角度分辨率的实验。
Techniques like ptychography and phase-contrast imaging, on the other hand, necessitate a wide dynamic range and single-electron sensitivity. The detector should be able to distinguish between 10,000 and one electron in this case.
Fast, Faster, Irrelevant
最佳帧速率范围是每秒10,000至100,000帧。下限旨在克服漂移，而上限是由实验的天然速度和可生成的逼真的束电流确定的，样品可以承受。
现在是一个大数据实验时代。随着检测器和计算技术的改善，获取的数据集变得越来越大。使用阳极，电解性阴极和覆盖层跨越数十微米，可以扫描固态电池的整个功能长度尺度。
这样的实验可能需要探测1,000×1,000或更多样品点。这是大量信息 - 有多个trabytes。另一方面，4D茎可以使用现代探测器处理如此大的视野。
Furthermore, STEM operations should be more automated. Technology is available to do this. The synchrotron beamline, for instance, is highly automated. As many of the steps performed — such as scanning the Regions of Interest (ROI) or performing drift correction — are highly repetitive, there is room for this in STEM.
Ophus博士对以数据为中心的开源代码特别感兴趣4D STEM analysis。
数据质量是许多当前分析的限制因素，数据数量是机器学习中的限制因素。但是，样品的质量限制了大多数实验。这是机器学习派上用场的地方，因为它即使从凌乱和厚实的样本中也可以产生准确的结果。
The authors have been tinkering with it, and the results are incredible — there are more upcoming publications. Dr. Ophus anticipates that in the future, machine-learning methods will be used to analyze difficult samples.
此信息已从Dectris Ltd提供的材料中采购，审查和调整（AN亚博网站下载interviewconducted with Dr Colin Ophus conducted by DECTRIS in April 2022).
For more information on this source, please visitDectris Ltd.