The Global Research Outreach Program is Samsung Electronics annual call for best and novel ideas. In image sensors, 6 areas of interest are identified:
1. High Resolution Computational Imaging based on Camera 2.0
Scope
We are interested in high resolution computational imaging with the following properties
Subject 2: Development of High Performance in Sub-1um Pixel and Simulation Environment
Scope
Challenges that significantly advance the state-of-the-art in pixel technologies include:
Subject 3: Energy Efficient Column Parallel Two Step ADCs for High Speed Imaging
Scope
We are interested in a two step ADC regarding the embodiment of low power & high speed CIS:
Subject 4: Resolution Enhancement of Image from Low Resolution Image
Scope
Challenges that significantly advance the state-of-the-art in image upscaling technologies include:
Subject 5: Smart Image Sensor
Scope
Subject 6: Si Photonic Biosensor for Healthcare
Scope
Interesting to note that 2012 is the first year program having a large image sensor section. The previous years programs had no CIS content.
1. High Resolution Computational Imaging based on Camera 2.0
Scope
We are interested in high resolution computational imaging with the following properties
- Methods to refocus and estimate depth from computational (plenoptic) camera and Camera 2.0 platform
- Methods to get high resolution output image and/or depth from plenoptic camera.
- Methods to operate computational camera in low power-consumption
- When implementing plenoptic camera as very thin camera module, are there any problem expected?
- There may be trade-off between output resolution and refocusing performance. How can we increase output resolution while keeping refocusing performance?
- Can super-resolution techniques increase output resolution? Are there any artifact or resource limitation from super-resolution?
Subject 2: Development of High Performance in Sub-1um Pixel and Simulation Environment
Scope
Challenges that significantly advance the state-of-the-art in pixel technologies include:
- New microlens structure to reduce diffraction and enhance light gathering efficiency in the submicron pixel sensor
- New methods to enhance light absorption power in the submicron pixel sensor
- New methods include new material as well as new optical structure
- Novel concepts (e.g. surface plasmon, multiple electron generation, etc.) are also welcomed.
- New color filter material and structure to improve SNR with good color accuracy
- Simulation method to increase speed and accuracy
- How can we control the diffraction of the lens between pixels to reduce the crosstalk in the submicron pixel sensor?
- How can the light gathering power of microlens be improved in the sub-micron pixels?
- How to remove loss of power in sub-micron pixels?
- Additional Structures to enhance the absorption power in the submicron pixel sensor.
- What is the ideal color filter spectrum to improve SNR?
- What is the best simulation method to improve speed and accuracy?
Subject 3: Energy Efficient Column Parallel Two Step ADCs for High Speed Imaging
Scope
We are interested in a two step ADC regarding the embodiment of low power & high speed CIS:
- Optimization of CIS readout architecture to overcome the two step ADC’s weakness
- ADC type to improve the productivity as well as size, speed, and power
- Structure innovation for energy-efficient two-step ADC having ultra low noise
- How can we get over all the obstacles, especially the trade-off relation between power, speed, and noise when designing a next CIS ADC?
- How can we secure the uniformity and productivity as well as IP’s performance?
- How can we get over the size competitiveness of original single slope ADC as well as power efficiency?
Subject 4: Resolution Enhancement of Image from Low Resolution Image
Scope
Challenges that significantly advance the state-of-the-art in image upscaling technologies include:
- Image upscaling based on self-similarity of an input image.
- Reducing artifacts and improve naturalness of the upscaled image
- Reducing required line memory and computational complexity of upscaling algorithm
- How to reduce artificial and unnatural representation of upscaled image to complex textured input image?
- How to define similarity among similar patterns? If we have similarity measure, how to use this similarity to stitch and upscale high resolution image?
- How to reduce computational redundancy in cascaded processing of self-similarity based upscaling?
Subject 5: Smart Image Sensor
Scope
- Advanced smart functional imaging technologies, especially in the field of health-care, natural user interface, virtual reality, etc.
- Pixel, circuit, image signal processing, optics, module and any other system level architectures covering the above mentioned area.
- Unprecedented pixel, circuit, and system level core technologies, such as three dimensional imaging, cognitive imaging, imaging in non-visible wavelength range, infrared-to-visible converting imaging, single transistor CMOS imaging, etc.
- Image signal processing algorithm which accounts for effectiveness in smart functionalities, such as smart pattern and motion recognition, etc.
- Methodology of analysis and characterization in pixel and system level for advanced smart imaging devices.
- Why do the new smart functionalities of proposition bear technological impact and possibly open new consumer electronics markets in regards of image sensor?
- How can the proposition be realized with new architectures?
- How can the proposition be realized in practice? For instance, is the proposition achievable with current CMOS technologies? Would the power of electrical consumption and operational speed be acceptable?
- Why do the methodology of analysis and characterization of proposition bear academic and technological importance and effectiveness?
Subject 6: Si Photonic Biosensor for Healthcare
Scope
- Smart biosensor technologies, especially in the area of disease detection such as cancer, virus, glucose and DNA sequencing for health-care
- Biosensing element and bio-processing, circuit and system level architectures covering the above mentioned area.
- Biomarker discovery for lung cancer diagnosis
- Photonics integrated circuits for biosensor, such as micro-optical spectrometer, WDM devices and optical ring resonator, etc.
- Circuit architectures, such as resonant wavelength sensing readout with low noise, etc.
- Methodology of analysis and characterization in bio-processing and system level for advanced smart biosensor.
- Measurement of the shift in resonant wavelength
- Miniaturized photonic components and biosensor element
- Compatibility with standard CMOS process
- Bio data processing algorithm which accounts for effectiveness in detection and DNA sequencing.
- Why the new functionalities of proposition should be realized with new architecture?
- How effectively the proposition can be realized in practice?
Interesting to note that 2012 is the first year program having a large image sensor section. The previous years programs had no CIS content.
