Archive for September, 2011

Herman PEEK : 50 years in silicon/CCD technology

Friday, September 30th, 2011

This week it is exactly 50 years ago that Mr. Herman Peek started working at Philips Research in silicon technology.  By it self nothing special of course, but keeping in mind that he still is active in silicon technology it is a more than remarkable achievement !  I had the luck of working with him from 1983 (when I joined Philips) till 2007 (when I left DALSA).  I do not recall all the history, but in 1983 Herman was already working on CCD technology when I met him for the first time.  He developed the semiconductor technology that was and still is applied to fabricate these great imaging devices.  Based on his ideas, Philips’ CCD reached world records in dark current as well as in area size.  Together with Herman (and many others) we developed the first full resolution HDTV sensors for broadcast use in the late ’80s and early ’90s.  The fundaments developed at that time can still be found in the today’s devices : stitching, tungsten strapping, low-noise output stage and dynamic pixel management.  After Herman reached the age of going after a pension, he stayed part-time on board as a consultant in CCD technology and he still is working part-time for Teledyne-DALSA (after the Philips’group was acquired by DALSA).  With this short message I would like to send my CONGRATULATIONS to Herman and would like to thank him for his great contributions to the CCD technology.  As Eric Fossum already stated during the last IISW, it is on the shoulders of these giants that we are standing.  Wish Herman a lot of pleasure in the years to come !

Albert, 30 September 2011.

How to measure the Fixed-Pattern Noise in Dark or DSNU (1)

Thursday, September 22nd, 2011

As a logical next step in the “How to Measure” discussion is to look after the non-uniformities in dark or Dark-Signal Non Uniformity (DSNU).  These are the variations of the dark signal from pixel to pixel.  It should be noted that the dark-signal itself can be composed out of :

       a thermal component, which will depend on the temperature and on the exposure time.  The thermal component will be present on pixel level,

       an electrical component, which is independent on the exposure time and almost independent on the temperatue.  This electrical component is due to threshold variations, gain variations and other imperfections in the electronic circuits.  It can be present on pixel, column and row level.

Just like in the case of the average dark signal, the non-uniformities are calculated (!) based on several images taken in dark conditions.  To limit the influence of any thermal noise component, several images need to be taken, preferably at various exposure or integration times.  Basically, the same data or images as used in the case of measuring the average dark signal can be reused.  So after averaging all images taken at a particular exposure time to reduce the thermal noise, calculations can take place on the averaged resulting image.

These averaged images are shown in Figure 1 : for 25 different exposure times, the result is visualized in the mosaic image.  Corresponding exposure times are indicated.


Figure 1 : dark images as a function of the exposure time, taken at 30 deg.C.

As can be seen from Figure 1, the sensor saturates at longer exposure times.  This is due to the limitation of the ADC in combination with the gain setting of the camera.  These effects will be explained and measured later in another blog.   

A first way of measuring/calculating the DSNU is to check its behavior as a function of exposure time.  These result of this is shown in figure 2.


Figure 2 : fixed-pattern noise in dark as a function of the exposure time and at 30 deg.C.

[!!!! there is a typo on the figure !!! the exposure dependent part = 0.0188 DN/ms !!!]

There are two curves shown :

       the first one contains all data delivered by the sensor, including the defects (black and white defects).  The latter ones hamper a linear behavior in the beginning of the curve.  And they do not allow the FPN to go to zero for high exposure times

       the second one is based on the same data after the correction of dead pixels.  This time the curve shows a better linearity as well as a fixed-pattern noise equal to zero for the highest exposure times.  Notice that in the latter case the output signal of the sensor is saturated by the ADC, and no FPN can be detected.

The FPN for zero exposure time, or the exposure time independent part of the FPN is found to be equal to 3.9 DN.  From the regression line calculated by means of the curve without defects, the time depending part of the FPN, being the dark current FPN, and equal to 0.019 DN/ms.

How to express the FPN of a sensor or a camera ?  As could be learned, part of the FPN is depending on the exposure time (and temperature), another part is not.  This makes the expression of the dark signal non-uniformity or DSNU not always uniform.  For example in the case illustrated, and specified at an average signal level of 25 % of saturation and an exposure time of 8 s, the DSNU can be expressed as :

       Maximum value of dark signal = 2905 DN and minimum value of the dark signal = 1373 DN.

       Peak-to-peak value of 1532 DN.

       Root mean square (rms) value equal to 150.5 DN.

Without knowing the average value of the dark signal, all these numbers are meaningless.  Because it is impossible to judge whether these values represent a large DSNU or not.  For that reason, taking the average dark signal of 1637 DN into account, the DSNU can also be expressed as 57.0 % of the average signal at 25 % of saturation.  This gives a much better judgement of the DSNU.

Notice that all these numbers represent the DSNU, but intrinsically they do not tell anything about the distribution of the dark fixed-pattern noise.  The right answer to this issue is to show the histogram of the dark signal.  Only in this way one can get a good idea about the randomness of the FPN in dark.  This is illustrated in Figure 3.


Figure 3 : histogram of the average signal in dark, taken at 30 deg.C.

The same data is shown in the two curves, but one on a linear vertical axis, the other one on a logarithmic vertical axis.  Notice that only in the latter case the outliers can be seen in the tail of the distribution !  Although not that many pixels have an outlier, they do show up immediately once the device is put into dark and they heavily contribute to the visual image quality.  So a logarithmic scale on the vertical axis should be the preferred way of dealing with histograms of average dark sensor signals.

 “There is a warning sign on the road ahead” :

       It is clear that the dark signal non-uniformities are depending on the exposure time, so accurately measuring or defining this parameter is very important,

       The average dark signal as well as the non-uniformities are exponentially depending on the temperature.  As a consequence, the temperature of the device under test needs to be stabilized with an accuracy of 1 deg.C, a change of 1 deg.C in device temperature can result in 10 % deviation of the dark signal.  Notice that the device temperature is not necessarily also room temperature or vice versa !  If the outcome of the average dark signal measurement is used for calibration purposes, the exact knowledge of the device temperature becomes of crucial importance,

       At this moment in the discussion, there is not yet any interest in the temporal noise behavior of the sensor or camera.  To limit the influence of the temporal noise on the measurements it is recommended to take many images in dark and averaged them on pixel level.  In this way the temporal noise will be averaged out,

       Fixed pattern noise can have several origins, it can come from the pixel, the row, the column, shading effects and saturation non-uniformities.  The split of the overall FPN into these various components will be discussed next time.

Albert, 19-09-2011.

Short feedback about SIVS : Swiss Image and Vision Sensors, September 8th, 2011.

Friday, September 9th, 2011


As far as I know, for the first time an image sensor workshop was organized by and at the ETH, Zurich in Switzerland.  Tobi Delbruck, opened the workshop by indicating that one of the workshop goals was to give an overview of the state-of-the-art of image sensors in Switzerland.  At the end of the workshop the conclusion could be that Tobi succeeded in reaching his target.  Not that much new items or technologies were disclosed, but several good speakers reviewed their technology and/or their products.

The session before lunch could be headed as : “What can/did we learn from the human retina ?”.  The first speaker taught us how to reverse engineer the human retina to get a better understanding about its functionality.  Next a couple of talks illustrated what already is implemented in silicon based on the working principle of the human retina.  Quite interesting to see how the human visual system is capable of transmitting and processing only useful information.  Or the human visual system only becomes active if there is really some new information observed.  In contradiction to that, the classical image sensors generate and deliver an incredible amount of redundant data and information.  The classical silicon image sensors are absolutely not efficient in the generation and transmission of data.  So here we still can learn a lot from mother nature.

An interesting lesson that could be learned after the morning session : even the most fanatic and academic researchers were complaining about the lack of marketing efforts to get their ideas, technologies, devices to the market.

The session after lunch and before the afternoon coffee break could be seen as a CSEM session.  Four different speakers (still with or no longer with CSEM or even still with but will shortly leave CSEM) highlighted particular aspects of the CSEM’s projects.  Nice talks, but most of them were already delivered at other conferences.  So basically not that much new information was presented, at least not for the people in the audience that regularly attend imaging conferences and workshops.

The last session of the SIVS workshop was devoted to speakers that talked about products that came recently on the market.  Some of the talks were illustrated by means of live demos.  And that always works : “Seeing is Believing”.  The workshop concluded with a short forum, a poster/demo session and a drink.

Conclusion : good overview of the imaging state-of-the-art in Switzerland, no groundbreaking technologies or ideas were disclosed, good atmosphere !  Thanks to Tobi and Shih-Chii for the organization.


Albert, 09-09-2011

New book on the market : “Single-Photon Imaging”

Monday, September 5th, 2011


I am very happy and pleased to announce the publication of a brand new book, entitled “Single-Photon Imaging”.  The book is published by Springer (ISBN 978-3-642-18442-0), and it is edited by my great friend Peter Seitz (CSEM and professor at the University of Neuchatel, both Switzerland) and myself.  I am not ashamed to tell that most of the work is done by Peter.  Without his drive and motivation it would have taken much longer to get the book ready.  It was really great working with Peter on this project.  The way of working with him reminds me about a Swiss watch : always on time.  Thanks Peter !

Also a big “Thank You” goes to the authors who wrote all the material.  I do realize that all of us are very busy and that writing a book chapter takes up a lot of time.  It should not be a big surprise that most of the authors put a lot of their private time in the writing of the material and this is highly appreciated.

Last but not least it is a pleasure to express my acknowledgement to all the people who are willing to buy and to read the book.  I do hope you will enjoy it !

For those interested, you can find more information about the book here.

Albert, 04-09-2011.