Welcome!

Microsoft Cloud Authors: Pat Romanski, Liz McMillan, Lori MacVittie, Elizabeth White, Yeshim Deniz

News Feed Item

Panasonic Develops Technology for Highly Sensitive Image Sensors Using Micro Color Splitters

Osaka, Feb 4, 2013 - (JCN Newswire) - Panasonic Corporation has developed unique "micro color splitters", which separate the light that falls on image sensors by exploiting light's wavelike properties. Applying them to actual image sensors allows bright color images to be achieved even under low-light conditions. This development makes color filters unnecessary by using the micro color splitters that control the diffraction(1) of light at a microscopic level. Panasonic has achieved approximately double the color sensitivity in comparison with conventional sensors that use color filters.

Image sensors are used in devices like smartphones, digital still cameras and video cameras, as well in security, vehicle parking, office, and healthcare applications - anywhere, in fact, that digital imaging is needed. Conventional color image sensors use a Bayer array(2), in which a red, green, or blue light-transmitting filter is placed above each sensor. These filters block 50 - 70% of the incoming light before it even reaches the sensor. Progress is being made in increasing the resolution of image sensors used in mobile and other devices by reducing pixel size, but demand for higher-sensitivity cameras is also increasing. Panasonic has developed a new technology that can be applied to existing or future sensors to enable them to capture uniquely vivid color images.

The developed technology has the following features.

1. Using color alignment, which can use light more efficiently, instead of color filters, vivid color photographs can be taken at half the light levels needed by conventional sensors.

2. Micro color splitters can simply replace the color filters in conventional image sensors, and are not dependent on the type of image sensor (CCD(3) or CMOS(4)) underneath.

3. Micro color splitters can be fabricated using inorganic materials and existing semiconductor fabrication processes.

This development is based on the following new technology.

1. A unique method of analysis and design based on wave optics that permits fast and precise computation of wave-optics phenomena.

2. Device optimization technologies for creating micro color splitters that control the phase of the light passing through a transparent and highly-refractive plate-like structure to separate colors at a microscopic scale using diffraction.

3. Layout technologies and unique algorithms that allow highly sensitive and precise color reproduction by combining the light that falls on detectors separated by the micro color splitters and processing the detected signals.

Panasonic holds 21 Japanese patents and 16 overseas patents, including pending applications, for this development.

This development is described in general terms in the Advance Online Publication version of Nature Photonics issued on February 3, 2013.

More on the Technology

1. Unique method of analysis and design based on wave optics permitting fast and precise computation of wave-optics phenomena

FDTD5 is widely used to analyze light in wave form, but its heavy computation workload has up to now made it impractical for designing micro color splitters. On the other hand, BPM(6) is an effective method of fast computation, but it has lower precision than FDTD and cannot accurately simulate color splitting. This prompted Panasonic to develop a practical and original design method that permits fast and precise computation of wave-optics phenomena. This technology allows the precise modeling of optical phenomena such as reflection, refraction, and diffraction by modeling spaces in regions with different optical constants and applying BPM to the spaces. This method can be applied not only to the design of micro color splitters, but can be extended to the design of other nano-scale optical processing systems.

2. Device optimization technologies leading to the creation of micro color splitters that control the phase of the light passing through a transparent and highly-refractive plate-like structure and use diffraction to separate colors on a microscopic scale

Color separation of light in micro color splitters is caused by a difference in refractive index between a) the plate-like high refractive material that is thinner than the wavelength of the light and b) the surrounding material. Controlling the phase of traveling light by optimizing the shape parameters causes diffraction phenomena that are seen only on a microscopic scale and which cause color separation. Micro color splitters are fabricated using a conventional semiconductor manufacturing process. Fine-tuning their shapes causes the efficient separation of certain colors and their complementary colors, or the splitting of white light into blue, green, and red like a prism, with almost no loss of light.

3. Layout technologies and unique algorithms that enable highly sensitive and precise color reproduction by overlapping diffracted light on detectors separated by micro color splitters and processing the detected signals

Since light separated by micro color splitters falls on the detectors in an overlapping manner, a new pixel layout and design algorithm are needed. The layout scheme is combined and optimized using an arithmetic processing technique designed specifically for mixed color signals. The result is highly sensitive and precise color reproduction. For example, if the structure separates light into a certain color and its complementary color, color pixels of white + red, white - red, white + blue, and white - blue are obtained and, using the arithmetic processing technique, are translated into normal color images without any loss of resolution.

(1) Diffraction: Behavior of light as a wave on the wavelength (nanometer) scale. Various phenomena occur when a wave encounters an obstacle.
(2) Bayer array: The arrangement of color filters used in most single-chip digital imaging sensors used in digital cameras, camcorders, and scanners to create a color image. The filter pattern is 50% green, 25% red and 25% blue.
(3) Charge Coupled Device Image Sensor (CCD sensor): A type of solid-state image sensing device for digital imaging, used in digital video cameras of all types. It has higher sensitivity and lower noise than other sensing devices.
(4) Complementary Metal Oxide Semiconductor Image Sensor (CMOS sensor): A solid-state image sensing device for digital imaging using CMOS.
(5) Finite-Difference Time-Domain method (FDTD): FDTD is a versatile modeling technique used to solve Maxwell's equations by spatial and temporal discretization.
(6) Beam Propagation Method (BPM): A numerical analysis technique in electromagnetics for solving the Helmholtz equation under conditions of a time-harmonic wave.

About Panasonic

Panasonic Corporation is a worldwide leader in the development and manufacture of electronic products in three business fields, consumer, components & devices, and solutions. Based in Osaka, Japan, the company recorded consolidated net sales of 7.85 trillion yen for the year ended March 31, 2012. Panasonic's stock is listed on the Tokyo, Osaka, Nagoya and New York (NYSE:PC) Stock Exchanges. The company has the vision of becoming the No. 1 Green Innovation Company in the Electronics Industry by the 100th year of its founding in 2018. For more information on Panasonic, its brand and commitment to sustainability, visit the company's website at http://panasonic.net/.



Source: Panasonic

Contact:
Tokyo Public Relations Office
Panasonic Corporation
Tel: +81-3-3574-5664
Fax: +81-3-3574-5699


Copyright 2013 JCN Newswire. All rights reserved. www.japancorp.net

More Stories By JCN Newswire

Copyright 2008 JCN Newswire. All rights reserved. Republication or redistribution of JCN Newswire content is expressly prohibited without the prior written consent of JCN Newswire. JCN Newswire shall not be liable for any errors or delays in the content, or for any actions taken in reliance thereon.

IoT & Smart Cities Stories
Bill Schmarzo, Tech Chair of "Big Data | Analytics" of upcoming CloudEXPO | DXWorldEXPO New York (November 12-13, 2018, New York City) today announced the outline and schedule of the track. "The track has been designed in experience/degree order," said Schmarzo. "So, that folks who attend the entire track can leave the conference with some of the skills necessary to get their work done when they get back to their offices. It actually ties back to some work that I'm doing at the University of ...
DXWorldEXPO LLC, the producer of the world's most influential technology conferences and trade shows has announced the 22nd International CloudEXPO | DXWorldEXPO "Early Bird Registration" is now open. Register for Full Conference "Gold Pass" ▸ Here (Expo Hall ▸ Here)
@DevOpsSummit at Cloud Expo, taking place November 12-13 in New York City, NY, is co-located with 22nd international CloudEXPO | first international DXWorldEXPO and will feature technical sessions from a rock star conference faculty and the leading industry players in the world. The widespread success of cloud computing is driving the DevOps revolution in enterprise IT. Now as never before, development teams must communicate and collaborate in a dynamic, 24/7/365 environment. There is no time t...
CloudEXPO New York 2018, colocated with DXWorldEXPO New York 2018 will be held November 11-13, 2018, in New York City and will bring together Cloud Computing, FinTech and Blockchain, Digital Transformation, Big Data, Internet of Things, DevOps, AI, Machine Learning and WebRTC to one location.
The best way to leverage your Cloud Expo presence as a sponsor and exhibitor is to plan your news announcements around our events. The press covering Cloud Expo and @ThingsExpo will have access to these releases and will amplify your news announcements. More than two dozen Cloud companies either set deals at our shows or have announced their mergers and acquisitions at Cloud Expo. Product announcements during our show provide your company with the most reach through our targeted audiences.
The Internet of Things will challenge the status quo of how IT and development organizations operate. Or will it? Certainly the fog layer of IoT requires special insights about data ontology, security and transactional integrity. But the developmental challenges are the same: People, Process and Platform and how we integrate our thinking to solve complicated problems. In his session at 19th Cloud Expo, Craig Sproule, CEO of Metavine, demonstrated how to move beyond today's coding paradigm and sh...
What are the new priorities for the connected business? First: businesses need to think differently about the types of connections they will need to make – these span well beyond the traditional app to app into more modern forms of integration including SaaS integrations, mobile integrations, APIs, device integration and Big Data integration. It’s important these are unified together vs. doing them all piecemeal. Second, these types of connections need to be simple to design, adapt and configure...
Cell networks have the advantage of long-range communications, reaching an estimated 90% of the world. But cell networks such as 2G, 3G and LTE consume lots of power and were designed for connecting people. They are not optimized for low- or battery-powered devices or for IoT applications with infrequently transmitted data. Cell IoT modules that support narrow-band IoT and 4G cell networks will enable cell connectivity, device management, and app enablement for low-power wide-area network IoT. B...
In his session at 21st Cloud Expo, Raju Shreewastava, founder of Big Data Trunk, provided a fun and simple way to introduce Machine Leaning to anyone and everyone. He solved a machine learning problem and demonstrated an easy way to be able to do machine learning without even coding. Raju Shreewastava is the founder of Big Data Trunk (www.BigDataTrunk.com), a Big Data Training and consulting firm with offices in the United States. He previously led the data warehouse/business intelligence and Bi...
Contextual Analytics of various threat data provides a deeper understanding of a given threat and enables identification of unknown threat vectors. In his session at @ThingsExpo, David Dufour, Head of Security Architecture, IoT, Webroot, Inc., discussed how through the use of Big Data analytics and deep data correlation across different threat types, it is possible to gain a better understanding of where, how and to what level of danger a malicious actor poses to an organization, and to determin...