Scanning Electron Microscopy (SEM) is critical in nanotechnology, materials science, and biological imaging due to its high spatial resolution and depth of focus. Signal-to-noise ratio (SNR) is an essential parameter in SEM because it directly impacts the quality and interpretability of the images. SEM is widely used in various scientific disciplines, but its utility can be compromised by noise, which degrades image clarity. This review explores multiple aspects of the SEM imaging process, from the principal operation of SEM, sources of noise in SEM, methods for SNR measurement and estimations, to various aspects that affect the SNR measurement and approaches to enhance SNR, both from a hardware and software standpoint. We review traditional and emerging techniques, focusing on their applications, advantages, and limitations. The paper aims to provide a comprehensive understanding of SNR optimization in SEM for researchers and practitioners and to encourage further research in the field.

Feedback is New Scientist's popular sideways look at the latest science and technology news. You can submit items you believe may amuse readers to Feedback by emailing feedback@newscientist.com Science is one of the most fruitful sources of new terminology. There's nothing like a surfeit of terms like "mitochondrial synthesis" and "quantum fluctuations" to make your writing sound authoritative Recently there has been a spate of scientific papers containing the phrase "vegetative electron microscopy/microscope". The term suggests a device for scanning broccoli, but it is utter nonsense. There are scanning electron microscopes and tunnelling electron microscopes, but not vegetative electron microscopes.

Sub-atomically focused electron beams in scanning transmission electron microscopes (STEMs) can induce a broad spectrum of chemical changes, including defect formation, reconfiguration of chemical bonds, and dopant insertion. Several groups have shown the feasibility of direct atomic manipulation via electron beam stimulation, which holds great promise for a number of downstream applications such as material design, solid-state quantum computers, and others (Jesse et al, 2018; Susi et al, 2017b; Dyck et al, 2017; Tripathi et al, 2018; Dyck et al, 2018). One of the challenges for advances in this space is that these types of atomic manipulation rely on manual control by highly-trained experts, which is expensive and slow. The ability to accurately automate this type of beam control could thereby result in tremendous impact on the feasibility of atomic manipulation for real use cases. A critical requirement for this automation is accurate estimation of the transition dynamics of atoms when stimulated by focused electron beams.

This paper introduces a new ontology for Materials Science Laboratory Equipment, termed MSLE. A fundamental issue with materials science laboratory (hereafter lab) equipment in the real world is that scientists work with various types of equipment with multiple specifications. For example, there are many electron microscopes with different parameters in chemical and physical labs. A critical development to unify the description is to build an equipment domain ontology as basic semantic knowledge and to guide the user to work with the equipment appropriately. Here, we propose to develop a consistent ontology for equipment, the MSLE ontology. In the MSLE, two main existing ontologies, the Semantic Sensor Network (SSN) and the Material Vocabulary (MatVoc), have been integrated into the MSLE core to build a coherent ontology. Since various acronyms and terms have been used for equipment, this paper proposes an approach to use a Simple Knowledge Organization System (SKOS) to represent the hierarchical structure of equipment terms. Equipment terms were collected in various languages and abbreviations and coded into the MSLE using the SKOS model. The ontology development was conducted in close collaboration with domain experts and focused on the large-scale devices for materials characterization available in our research group. Competency questions are expected to be addressed through the MSLE ontology. Constraints are modeled in the Shapes Query Language (SHACL); a prototype is shown and validated to show the value of the modeling constraints.

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With resolution 1,000 times greater than a light microscope, electron microscopes are exceptionally good at imaging materials and detailing their properties. But like all technologies, they have some limitations. To overcome these limitations, scientists have traditionally focused on upgrading hardware, which is costly. But researchers at the U.S. Department of Energy's (DOE) Argonne National Laboratory are showing that advanced software developments can push their performance further. Argonne researchers have recently uncovered a way to improve the resolution and sensitivity of an electron microscope by using an artificial intelligence (AI) framework in a unique way.

On a chilly evening last fall, I stared into nothingness out of the floor-to-ceiling windows in my office on the outskirts of Harvard's campus. As a purplish-red sun set, I sat brooding over my dataset on rat brains. I thought of the cold windowless rooms in downtown Boston, home to Harvard's high-performance computing center, where computer servers were holding on to a precious 48 terabytes of my data. I have recorded the 13 trillion numbers in this dataset as part of my Ph.D. experiments, asking how the visual parts of the rat brain respond to movement. Printed on paper, the dataset would fill 116 billion pages, double-spaced. When I recently finished writing the story of my data, the magnum opus fit on fewer than two dozen printed pages. Performing the experiments turned out to be the easy part. I had spent the last year agonizing over the data, observing and asking questions. The answers left out large chunks that did not pertain to the questions, like a map leaves out irrelevant details of a territory.

Google has helped create the most detailed map yet of the connections within the human brain. It reveals a staggering amount of detail, including patterns of connections between neurons, as well as what may be a new kind of neuron. The brain map, which is freely available online, includes 50,000 cells, all rendered in three dimensions. They are joined together by hundreds of millions of spidery tendrils, forming 130 million connections called synapses. The data set measures 1.4 petabytes, roughly 700 times the storage capacity of an average modern computer.

Last Tuesday, teams from Google and Harvard published an intricate map of every cell and connection in a cubic millimeter of the human brain. The mapped region encompasses the various layers and cell types of the cerebral cortex, a region of brain tissue associated with higher-level cognition, such as thinking, planning, and language. To make the map, the teams sliced donated tissue into 5,300 sections, each 30 nanometers thick, and imaged them with a scanning electron microscope at a resolution of 4 nanometers. The resulting 225 million images were computationally aligned and stitched back into a 3D digital representation of the region. Machine learning algorithms segmented individual cells and classified synapses, axons, dendrites, cells, and other structures, and humans checked their work.

To make the top-of-the-line chips for Apple's iPhone, such as the A14, or Nvidia's A100 series AI processors, with billions of transistors, it takes a factory that costs $16 billion to build and maintain. That amount is up from $10 billion just eight years ago, and is set to rise significantly again, to perhaps $18 billion in the next few years. That has presented the chip industry with a quandary: Such chips more than ever need to be checked for defects, but chip makers are under more pressure than ever to get product out the door to recoup their investment. "You should naturally want to inspect more, because there are more process steps, more things that can go wrong, but if you look at what has happened, the economics have prohibited our customers from doing that inspection," said Keith Wells, who is group vice president of the imaging and process control group at Applied Materials, the biggest maker of tools for making chips. "We see this need to really solve this economic problem for our customers," said Wells, who spoke with ZDNet via Zoom.