News Article | May 16, 2017
HOLON, Israel & SAN JOSE, Calif.--(BUSINESS WIRE)--Optimal+, the leader in big data analytics for semiconductor and electronics manufacturing operations, today announced it has been named a Gartner “Cool Vendor” in IoT Analytics, for 2017. Optimal+ is one of four companies profiled in the report and was recognized for its innovative, intriguing and impactful data analytics platform. As we enter the era of the Internet of Things and Industry 4.0, achieving the highest quality standards without compromising time-to-market becomes an ever-increasing concern for semiconductor and electronics brand owners. Optimal+ offers the only big data software solution in the market that focuses on leveraging product analytics to improve yield and quality metrics in semiconductor and electronics manufacturing operations. “General-purpose modern analytics tools can be used for the IoT in most instances, but two differences specific to IoT analytics — new analytics users and massive amounts of sensor data coming at high speed — are best addressed by capturing domain knowledge and incorporating it into advanced analytics”, according to co-authors Jim Hare and Simon Jacobson, Research VPs at Gartner. “The product genealogy — combined with all of the test results collected throughout the value chain, and the respective environmental data — is fed downstream in the supply chain to create a ‘product DNA’ that can be used to reduce product recalls, improve the product's user experience, and protect the brand.” “In today’s complex electronics ecosystem, analyzing and understanding the DNA of every product and how its quality and performance impacts the supply chain is vital,” said Dan Glotter, Founder and CEO of Optimal+. “Our big data solutions are providing traceability throughout the supply chain to improve quality and deliver brand protection to semiconductor and electronics manufacturers.” According to Gartner, the “Cool Vendors” in Internet of Things Analytics 2017 profiles innovative vendors in IoT analytics that focus on some of the hottest areas of IoT – visibility into the manufacturing process, enabling new analytics users, and device diagnostics, repair and maintenance – to help data and analytics leaders increase the value of IoT projects. The “Cool Vendors” reports reflect IT products and services that Gartner finds interesting and innovative. Because of the breadth and depth of knowledge and expertise in technology and business, Gartner is in a unique position to identify and evaluate up and coming vendors across markets, topics, and industries. To view the full Gartner “Cool Vendors” in IoT Analytics 2017 report, click http://bit.ly/2qmbjra. Disclaimer Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. About Optimal+ Optimal+ was founded in 2005 with the vision of uniting global manufacturing data with human expertise to deliver actionable knowledge that enables engineers to make better decisions, faster. Our Manufacturing Intelligence™ Platform combines machine-learning algorithms with a global data infrastructure to drive real-time product analytics that extract hidden insights from the data silos of any semiconductor or electronics supply chain. The company analyzes more than 50 billion devices every year on behalf of Fortune 500 companies such as AMD, NVIDIA, Qualcomm and ST Microelectronics to enhance quality and yield and deliver comprehensive brand protection for all their products. For more information, visit www.optimalplus.com. Follow us on Twitter @OptimalPlus.
Okada M.,University of Hyogo |
Okada M.,Chiyoda Corporation |
Kishiro T.,HOLON Co. |
Ataka M.,HOLON Co. |
And 3 more authors.
Microelectronic Engineering | Year: 2011
Nanoimprint lithography (NIL) is a simple process to fabricate nanostructure devices with high throughput and low cost. The mold fabrication process is an important factor to improve NIL technology. Adopting a seamless pattern mold fabrication process is expected to enhance throughput. In this study, we developed a seamless long line and space (L and S) pattern fabrication process by using an electron beam (EB) stepper. As a result, a seamless 200 nm line and 300 nm space pattern (120 mm long and 10 mm wide) was exposed on a 6 in. Si wafer. We also carried out thermal nanoimprinting using the seamless L and S pattern mold fabricated by using the EB stepper. We confirmed that the seamless L and S pattern was clearly imprinted on the resin. © 2011 Elsevier B.V. All rights reserved.
Okada M.,University of Hyogo |
Okada M.,Chiyoda Corporation |
Kishiro T.,HOLON Co. |
Yanagihara K.,HOLON Co. |
And 3 more authors.
Journal of Vacuum Science and Technology B:Nanotechnology and Microelectronics | Year: 2010
The nanoimprint molds are usually fabricated by electron beam (EB) lithography. In recent years, a large-area mold fabrication with a high throughput is required to use nanoimprint lithography to produce devices in mass production. Using a conventional EB system to fabricate a large-area mold requires a very long exposure. To shorten the time, the authors have newly developed an EB stepper. The hole pattern (300 nm hole and 600 nm pitch) was exposed on a whole 4 in. SiO2/Si substrate with ZEP-520A (ZEON Co.) positive resist by the stepper. The exposure time was only 15 min. After this exposure, the authors did reactive ion etching and thermal nanoimprinting by using the 4 in. SiO2/Si mold. The whole patterns on the 4 in. SiO2/Si mold were imprinted on a 4 in. Si substrate coated with ZEP-520A. © 2010 American Vacuum Society.
Tian M.,Semiconductor Manufacturing International |
Wang J.,Holon Co. |
Bandoh H.,Holon Co. |
Guo E.,Semiconductor Manufacturing International |
Lu M.,Semiconductor Manufacturing International
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2015
Mask pattern measurement becomes one of the main challenges for the quality evaluation of the mask which is applied with complex lithography optical effect correction. Traditional straight edge mask pattern is evaluated with 1-dimension Critical Dimension (CD) method. But for 2-dimension pattern especially the mask full filled with complex shapes OPC pattern, many special approaches are studied attempt to characterize 2D pattern from different points of view [1-5]. A simple CD's information and the traditional mask performance evaluation parameters, such as CD mean-to-target and CD uniformity, are no longer suitable to such 2D pattern due to lacking of the pattern's character descriptions. Therefore the CD performances may not represent the actual wafer printing result in many cases. In addition, non-straight pattern edge induces significant CD measure error which makes it difficult to clarify the real mask pattern making quality. This paper investigates a pattern contour based solution for 2D structure performance evaluation. The basic contours of GDS and CD-SEM image are extracted, overlapped and processed and then the edge roughness of SEM contour and the bias between the above two kinds of contour are adopted on 2D individual pattern performance's statistics. By utilizing this solution, the 2D pattern quality can be described quantitatively as two main aspects, shape and size with the results of edge roughness and bias. Generalize this solution, the 2D pattern's uniformity, mean size, or other performances, can be evaluated quantitatively in the similar way as well. This solution calculation bases on pattern contour, therefore the measure pattern is not restricted by its shape. © 2015 SPIE.
Yamada K.,Holon Corporation |
Kitayama Y.,Holon Corporation |
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2011
EUV lithography is expected to begin production in 2014. Production of successful EUV photomasks requires patterned mask inspection (PMI). The ultimate PMI tool is expected to utilize actinic (EUV) illumination. Development of such a tool is expected to require three years after funding. Current test EUV masks, such as 22 nm, can be inspected using 193 nm wavelength deep UV (DUV) inspection tools similar to those currently being used for DUV masks. The DUV inspection tools may be extended for the 16 nm node. However EUV production is expected to start with 11 nm node masks which cannot be inspected with proposed DUV inspection tools. Therefore E-beam inspection (EBI) is discussed as the interim PMI method. EBI has the advantage of high resolution and the disadvantages of low inspection speed and relative insensitivity to ML defects (in the multi-layer material). EBI inspection speed is limited by the pixel size, pixel capture rate and the number of electron columns. The pixel rate is limited by the detector time-resolution, the beam current, and the detection efficiency. Technical improvements in beam focus, secondary electron detection, and defect detection and analysis provide good performance for 22 nm node masks. We discuss the advances and show that performance can be extrapolated for 16 and 11 nm node patterned mask inspections. We present sensitivity and false-defect frequency results of using the Holon EBI tool on 22 nm test masks and a roadmap for extending its operation for use on 16 and 11 nm node masks for inspections requiring 2-5 hours per mask. © 2011 SPIE.
Santo I.,HOLON Corporation |
Higuchi A.,HOLON Corporation |
Anazawa M.,HOLON Corporation |
Bandoh H.,HOLON Corporation
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2014
Contour extraction of complicated optical proximity correction (OPC) patterns for advanced photomasks is increasingly needed in addition to the conventional mask CD measurement. The lithography simulation based on contour extraction from the SEM images on photomasks is one of the efficient methods to assure adequacy of OPC patterns. In this paper, the function of the above-mentioned contour extraction, and the performance requirements for the CD-SEM for this function using Mask CD-SEM 'Z7', the latest product of HOLON, and the scheme to correct the distortion are explained. Furthermore, the perspectives of the application of our contour extraction method are outlined. © 2014 SPIE.