Avi Strum about : “High end image sensors challenges”. Avi mentioned the demands for the high-end application imagers, being :
– Sensitivity, over 15 stops for digital cinema,
– Dynamic range, up to 120 dB for automotive and digital cinema,
– Frame rate driven by the need of multiple exposures,
– High resolution driven by smaller pixels,
– Functionality to allow in-pixel computational analysis,
– Angular response driven by the application of DSLRs.
The answer to all these challenges is BSI. Avi spent most of its time to the explanation of the BSI process developed by TowerJAZZ (in combination with a partner SOITEC). Main challenges seen are :
– the back-thinning of the silicon (using SOI and stopping the etch on the BOX),
– the alignment on the backside for post processing (deep trenched alignment markers),
– suppression of the dark current (perfect interface with smart cut of SOITEC and buried AR layers).
According to the speaker, this process will be more expensive than others, but it will have outstanding image performances compared to bulk BSI CMOS. It is expected that the process will become available to customers in the beginning of 2012.
During Q&A it was learned that the process will run on 200 mm wafers and will be stitchable compatible.
Next was Shorab Yaghmai (Aptina) : “Technology trends and market opportunities for image sensors for automotive applications”. One of the key requirements for automotive is the dynamic range. In one of their first automotive products Aptina tried to obtain a larger dynamic range by means of multiple exposure. Advantages are no added pixel circuitry, CDS readout and no reduction in full well. Disadvantages : on-chip line buffers needed and 3x faster readout needed. Another method for WDR is based on the lateral overflow with soft TX pulse : the latter modulates the full well capacity of the pinned photodiode during the exposure period. Both techniques have their own pros and cons. Aptina developed dedicated postprocessing of the images to cope with additional blue blur (due to blooming in the different colours and different exposure cycles) and motion blur.
Another challenge for automotive is the low light, low noise imaging needs. Improvement techniques in this field are dual conversion gain pixels, pixel-wise gain selection, low noise analogue design (who is not willing to have this ?), digital CDS, digital FPN measurement and correction schemes, randomizing readout channels (thank you Martijn Snoeij !?), isolation of analogue and digital circuitry.
All these techniques are applied in new Aptina devices : 1Mpixel with 120 dB dynamic range with compressed and uncompressed data out.
“High-Speed Imaging”, by Thomas Baechler (CSEM).
Started with a nice historic overview of high-speed camera, starting from Albert Londe’s camera in 1893 with 12 images in 0.1 s, to a frame rate of 16 Mimages/s by G. Etoh. Biggest issue these days in continuous shooting at high frame rates is the data rate to read all pixels at ultra-high speeds. For this reason data reduction is mandatory. A few possible data reduction approaches were discussed : contrast images, delta images, adaptive ROI, optical correlation and spectral methods. An example of optical coherence tomography was shown with an intelligent data reduction available, for a camera which normally needed a frame rate of 400 kHz. The frame rate is reduced with pixel level demodulation in a kind of smart pixel. The pixels are 40 um in size, and contain 1 PD and 30 transistors and capacitors per pixel. This design reduces the data rate by a factor of 100 (or even more) of the original requirement.
Hendrik Malm (Nocturnal Vision) : “Novel technology for nocturnal vision applications”. This paper primarily deals with image processing on data that is generated by an existing video camera. The data is processed in a combined contrast enhancement, image stabilization and noise reduction in a novel way : spatiotemporal intensity averaging with sharpening. The talk was supported by video shots to illustrate each step of the processing. In principle the idea comes down to the use of multiple images (minimum 7 frames in the time domain, 9 x 9 kernels in spatial domain) from a video stream to improve every individual image. The whole concept is inspired by animal vision. The algorithm can be implemented in a real-time processor.
Peter Centen (Grass Valley) : “Advances in image sensor technology for broadcast cameras”.
Broadcast has to deal with the following specs : illumination range 0.1 lux … 2000 lux … 100000 lux, colour temperature 1800K … 3200K … 20000K, gain -6dB … 18 dB … 40 dB, video up to 60 fr/s, modulated light sources (fluorescent). On the other hand, broadcast is full of existing standards that needs to be obeyed because cameras have to be synchronized by each other, but also by the studio and the broadcasting system. Next, the SNR measurement is clearly described how to perform.
Advances in CCD technology reported for broadcast applications : image diagonal remained fixed, light conditions remained fixed, video bandwidth and pixel size increased, overall 15 dB in noise and sensitivity is gained over the last 20 years. For instance : read noise from 30 e- (5 MHz) to 8 e- (30 MHz).
Broadcast is a low volume, high performance market : difficult to find a CMOS imager, as a result Thomson started his own development. Future of broadcast sensors : imager with additional features, single image for 3D, HDR live video, high speed.
During Q&A Peter compared CCDs with CMOS for his application : pros of the CCD are the absence of any row noise, absence of any column noise; pros of the CMOS are the low temporal noise due to the parallel processing on column level, single supply voltage and the plug-and-play way of operation.
Albert 23-03-2011.