#Terahertz Wafer Scanner Market

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Terahertz Wafer Scanner Market – Global Industry Analysis, Trends, and Forecast 2026 Terahertz wafer scanner is commonly used to detect chips defects during manufacturing of chips. Wafer technology detects daunting challenges during wafer fabrication process of chip manufacturing.

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Terahertz Wafer Scanner Market Surge At A Robust Pace In Terms Of Revenue

Terahertz wafer scanner is commonly used to detect chips defects during manufacturing of chips. Wafer technology detects daunting challenges during wafer fabrication process of chip manufacturing. Moreover, to reduce wafer fab rejection during manufacturing time, terahertz wafer technology scanner has been developed by scanner manufacturer across the globe. Terahertz scanner is also used to detect the thickness of materials. Furthermore, semiconductor technology is pushing prevailing test frequency limitations to the terahertz (THz) extremes. THz frequency is utilized by new applications for semiconductor materials, military and aerospace, medical imaging, automotive, wider communication bandwidths, and other emerging technologies. The global Terahertz wafer scanner market is expected to witness a steady growth during the forecast period from 2018 to 2026.

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In global terahertz wafer scanner market, there are few highly cost and highly sensitive product is available to detect the unseen layer of wafer.  Subsequently, end of the final device wafer will fail for the reason that the assessment system was not able to detect the minor and unseen defects. Terahertz wafer scanner detects chip defects down to 1 nm resolution. Rapid growth of the semiconductor industry, due to rise in demand for consumer electronic devices is anticipated to accelerate the demand of global terahertz wafer scanner market during the forecast period from 2018 to 2026. Moreover, rising installation of chip technology across various semiconductor sectors in different parts of the application in order to reduce manufacturing cost and to eliminate inefficiencies in manufacturing is also predicted to trigger the demand of terahertz wafer scanner market in the coming years.

The global terahertz wafer scanner market is primarily driven by semiconductor manufacturing industry for detecting defects of wafers on the surface. This in turn is acting as a driving factor in the growth of the terahertz wafer scanner market in the coming years. Terahertz wafer scanner not only inspect the surface but also the sub surface of interior layer which is anticipated to create new opportunity for this product manufacturers in future. In order to obtain, optical inspection system perceive on the surface and the resolution limitation of this product are also hindering the demand of this technology during the forecast period. For providing detailed overview of the terahertz scanner scan and penetrate all materials except metals which is expected to dominate the market during the forecast period. Any defects such as cracks, inclusions, very small particulate, and non-uniformity of material is easily and clearly spotted and recognized by using these product. Furthermore, scientists have demonstrated that terahertz scanner used in art restoration which is an opportunities for this product market during the forecast period. On the flip side, higher installation and maintenance cost is hindering the growth of the market.

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New Artificial Intelligence Device Identifies Objects at the Speed of L industrial iot connectivityight

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The network, compose IoT Remote Monitoring  d of a series of polymer layers, works using light that travels through it. Each layer is 8 centimeters square. Ozcan Research Group/UCLA

A team of UCLA electrical and computer engineers has created a physical artificial neural network — a device modeled on how the human brain works — that can analyze large volumes of data and identify objects at the actual speed of light. The device was created using a 3D printer at the UCLA Samueli School of Engineering.

Numerous devices in everyday life today use computerized cameras to identify objects — think of automated teller machines that can “read” handwritten dollar amounts when you deposit a check, or internet search engines that can quickly match photos to other similar images in their databases. But those systems rely on a piece of equipment to image the object, first by “seeing” it with a camera or optical sensor, then processing what it sees into data, and finally using computing programs to figure out what it is.

Schematic of the optical neural network: The digit “5” is sent as a light signal through the layers comprised of artificial neurons. That light bounces around as it travels through the layers, but when it exits, the brightest signal is picked up by the detector looking only for a “5.” Ozcan Research Group/UCLA

The UCLA-developed device gets a head start. Called a “diffractive deep neural network,” it uses the light bouncing from the object itself to identify that object in as little time as it would take for a computer to simply “see” the object. The UCLA device does not need advanced computing programs to process an image of Transformer Monitoring   the object and decide what the object is after its optical sensors pick it up. And no energy is consumed to run the device because it only uses diffraction of light.

New technologies based on the device could be used to speed up data-intensive tasks that involve sorting and identifying objects. For example, a driverless car using the technology could react instantaneously — even faster than it does using current technology — to a stop sign. With a device based on the UCLA system, the car would “read” the sign as soon as the light from the sign hits it, as opposed to having to “wait” for the car’s camera to image the object and then use its computers to figure out what the object is.

Technology based on the invention could also be used in microscopic imaging and medicine, for example, to sort through millions of cells for signs of disease.

The study was published online in Science on July 26.

“This work opens up fundamentally new opportunities to use an artificial intelligence-based passive device to instantaneously analyze data, images and classify objects,” said Aydogan Ozcan, the study’s principal investigator and the UCLA Chancellor’s Professor of Electrical and Computer Engineering. “This optical artificial neural network device is intuitively modeled on how the brain processes information. It could be scaled up to enable new camera designs and unique optical components that work passively in medical technologies, robotics, security or any application where image and video data are essential.”

The process of creating the artificial neural network began with a computer-simulated design. Then, the researchers used a 3D printer to create very thin, 8 centimeter-square polymer wafers. Each wafer has uneven surfaces, which help diffract light coming from the object in different directions. The layers look opaque to the eye but submillimeter-wavelength terahertz frequencies of light used in the experiments can travel through them. And each layer is composed of tens of thousands of artificial neurons — in this case, tiny pixels that the light travels through.

Together, a series of pixelated layers functions as an “optical network” that shapes how incoming light from the object trave smart vending  ls through them. The network identifies an object because the light coming from the object is mostly diffracted toward a single pixel that is assigned to that type of object.

The researchers then trained the network using a computer to identify the objects in front of it by learning the pattern of diffracted light each object produces as the light from that object passes through the device. The “training” used a branch of artificial intelligence called deep learning, in which machines “learn” through repetition and over time as patterns emerge.

“This is intuitively like a very complex maze of glass and mirrors,” Ozcan said. “The light enters a diffractive network and bounces around the maze until it exits. The system determines what the object is by where most of the light ends up exiting.”

In their experiments, the researchers demonstrated that the device could accurately identify handwritten numbers and items of clothing — both of which are commonly used tests in artificial intelligence studies. To do that, they placed images in front of a terahertz light source and let the device “see” those images through optical diffraction.

They also trained the device to act as a lens that projects the image of an object placed in front of the optical network to the other side of it — much like how a typical camera lens works, but using artificial intelligence instead of physics.

Because its components can be created by a 3D printer, the artificial neural network can be made with larger and additional layers, resulting in a device with hundreds of millions of artificial neurons. Those bigger devices could identify many more objects at the same time or perform more complex data analysis. And the components can be made inexpensively — the device created by the UCLA team could be reproduced for less than $50.

While the study used light in the terahertz frequencies, Ozcan said it would also be possible to create neural networks that use visible, infrared or other frequencies of light. A network could also be made using lithography or other printing techniques, he said.

The study’s others authors, all from UCLA Samueli, are postdoctoral scholars Xing Lin, Yair Rivenson, and Nezih Yardimci; graduate students Muhammed Veli and Yi Luo; and Mona Jarrahi, UCLA professor of electrical and computer engineering.

The research was supported by the National Science Foundation and the Howard Hughes Medical Institute. Ozcan also has UCLA faculty appointments in bioengineering and in surgery at the David Geffen School of Medicine at UCLA. He is the associate director of the UCLA California NanoSystems Institute and an HHMI professor.

Publication: Xing Lin, et al., “All-optical machine learning using diffractive deep neural networks,” Science 26 Jul 2018: eaat8084; DOI: 10.1126/science.aat8084

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