Electronic Imaging 2014 (2)

February 8th, 2014

Boukhayma (CEA-Leti) presented a very nice paper about the noise in PPD-based sensors.  He modelled the electronic pixel components for their noise performance, and based on this analysis he handed out some guidelines to limit the noise level in the pixels.  Although not all conclusions are/were new, it was nice to see them all listed in one presentation and supported by simulation results : lower the FD node capacitance, lower the width of the SF transistor, choose a p-MOS transistor as SF because an n-MOS transistor will give too much 1/f noise, optimize the length of the SF (depending on gate-drain capacitance, gate-source capacitance, FD capacitance and width of the transistor, the formula for the optimum gate length was shown).  If the thermal noise of the pixel is dominant, it does not matter whether to use a simple SF in the pixel or to use an in-pixel gain stage.  But if the 1/f noise is dominant, one should avoid a standard n-type MOS transistor.

Angel Rodriguez-Vazques (IMSE-CNM, Spain) gave a nice overview of ADC architectures when used for image sensors.  It is a pity that for such an overview paper only 20 minutes presentation time were provided.  These kind of overview papers deserve to get more time.

Seo (Shizuoka University, Japan) described a pixel without STI, but with isolation between the pixels based on p-wells and p+ channel stops.  The omission of the STI has to do with the dark current issues that come together with the STI.  The authors showed a very cute lay-out of a 2×2 shared pixel concept (1.75 T/cell).  All transistors and transfer gates were ring shaped, located in the center of the 2×2 pixel and with the 4 PPDs at the outside, looks a bit like a spider with only 4 legs.  The pixels were pretty large (7.5 x 7.5 um2), in combination with a relatively low fillfactor of 43 %, as well as a low conversion gain of 23 uV/electron.  Of course the ring structure of the output transistors consumes a large amount of silicon, and seems to result in a relative large floating diffusion capacitance.  The dark current is reduced by a factor of 20 (compared to the STI-based sensor), down to 30 pA/cm2, QE = 68% @ 600 nm.

It is hard to decide who will win the Award for the most artistic pixel lay-out : the hedgehog of Tohoku University or the spider of Shizuoka University ?  But it any case, the award goes to a Japanese university.  Great work !

Albert

February 7th, 2014.

Electronic Imaging 2014 (1)

February 7th, 2014

An interesting paper of Tohoku University was presented at the EI14.  They published their paper about a 20M frame/s sensor already a while ago at the ISSCC, but they never disclosed the pixel structure to empty the PPD within the extremely short frame times.  The EI14 paper was focusing on the pixel architecture and specifically on the PPD structure.  Miyauchi explained that two technological “tricks” are applied to create an electric field in the PPD to speed up the transfer of the photon-generated electrons from the PPD to the FD node.  Firstly a gradient in the n-doping is implemented by using three different n-dopings, secondly the n-regions are not simple rectangulars or a squares, but have the look of hedgehogs with all kind of sharp needles extending away from the FD node.  On one hand the lay-out of the triple n-implantation looks quite complicated, on the other hand it looks quite funny as well, but after all, it seems to be effective.

Simulations as well as measurement results were shown.  Simulated was a worst-case transfer time of 9ns, measured was a transfer time of about 5 ns.  These are very spectacular results taking into account that the pixel size is 32 x 32 um2.  As far as overall speed of the sensor is concerned : 10M frames/s are reported for a device with 100k pixels, 128 on-chip storage nodes for every pixel and a consumed power of 10W.  The device can also run in 50k pixels mode, with the same power consumption but then with a frame rate of 20M frames/s and with a storage capacity of 256 frames on-chip.

 

There were two papers that used the same image sensor concept : allow the pixels to integrate up to a particular saturation level, and record the time it takes to come to this point.  This idea is not really new (was it Orly who did this for the first time in her conditional reset idea ?), but the idea in which this concept is applied seems to be new.

El-Desouki (King Abdulaziz City for Science and Technology, Saudi Arabia) is using SPADs and is allowing the SPADs to count up its events to a certain defined number to convert an amount of light into a time slot, measures this time slot by converting it into the digital domain and is sending out this data.  A further sophistication of the idea is not to count in the digital domain (it needs too many transistors per pixel) but to do the counting in the analog domain.  Finally the author explained how one can make a TDI sensor based on this concept.

A bit more “out-of-the-box” was the concept introduced by Dietz (University of Kentucky).  Allow the pixels to integrate up to a certain level (e.g. saturation), record the time it takes to reach that point, and perform this action continuously in the time domain.  In this way one gets, for each pixel, a kind of analog signal describing the behavior of each pixel in the time domain.  This way of operating the pixel makes the sensor completely free of any frame rate.  If an image is needed, one can take whatever timeslot in the time domain that is recorded, take the analog signal out of the memory, and average the analog signal within this timeslot.  Of course every pixel needs a lot of processing as well as a huge storage space to record its behavior in the time domain.  But with the stacked concept of imager-processor-memory, the speaker was convinced that in the future this should be feasible.

Yonai (NHK, Japan) presented some new results obtained with the existing 33M UHDT sensor, already presented earlier in WKA winning paper.  But this time the authors changed the timing diagram such that the sensor was allowed to perform digital CDS off-chip.  Results : 50 times reduction in FPN (down to 1 electron) and 2 times reduction in thermal noise (down to 3 electrons @ 60 fr/s).

Kang (Samsung) presented some further sophistication of the RGB-Z sensor that was already presented by Kim at the ISSCC.  From one single imager, a normal RGB image can be generated, as well as a depth-map by using the imager in a ToF mode.  The author presented a simple, but intelligent technique to improve the performance of the device by removing any asymmetry in pixel design/lay-out/fabrication.  The technique applied is simply reversing the Q0 and Q180 from frame to frame.  Actually the technique looks very much the same as chopping in analog circuitry.

 

Albert

February 7th, 2014.

Merry Christmas and Happy New Year

December 20th, 2013

Good Bye 2013 !  The year is almost over.  And as I did in the foregoing years, also this time I would like to take a quick look back and see what 2013 brought to us.  Also now I can repeat that the year 2013 was again a great year for Harvest Imaging !  The year started with the move towards a new office space.  In the meantime all furniture, equipment and infrastructure is installed and in operation.  So most of the blogs you could read this year were “born” from my new office space.  This is especially true for the blogs that contained measurement data.

If I overlook the “products” of Harvest Imaging, I can split them up into three groups :

  • The training courses, in-house as well as public courses.  It is and remains amazing and sometimes hard to believe where all the people are coming from that attend the courses.  In 2013 I had a training almost every other week, and I just completed course number 150 !  It is very motivating to experience that so many young engineers step into the challenging but very rewarding world of imaging,
  • The consulting activities.  I hope that my readers do understand that I cannot elaborate on this because of confidentiality reasons.  But I can indicate that my expertise was used in the field of imaging technology as well as intellectual property related projects,
  • The new product of Harvest Imaging, being the organization of the Solid-State Imaging Forum.  The very first edition of this forum was organized this December, focusing on “ADCs for Imagers”.  It was really a success and the large attendance proofs that there is a need for this kind of in-depth information and knowledge exchange.

To conclude this overview of products, it is a pleasure for me to thank all my customers who brought business to Harvest Imaging, in one way or another.  It is great to experience your trust and confidence by consulting the expertise of Harvest Imaging.  Thanks very much !

2013 is an odd number, and it inherently translates into another International Image Sensor Workshop, this time in the USA.  My friends in the field, Boyd Fowler, Eric Fossum and Gennadyi Agranov, organized another great Workshop.  Location was Snow Bird in Utah, where all technical information was exchanged, distributed and absorbed (literally) at a very high level.  Although again the technical and scientific level of the Workshop was outstanding, the highlight for me was the “meet and greet” with Michael Tompsett, the real inventor of the CCD image sensor.  He gave a very impressive overview of his history in the CCD imaging world and clearly explained to the audience that the 2009 Nobel Prize for the invention of the CCD image sensor went to the wrong person.  Thanks to the chairs of the Workshop to take the initiative to invite Michael Tompsett !

To conclude, I wish all of you the very best for 2014, and hope that we will regularly “meet” through this blog.  Thanks for visiting the website of Harvest Imaging, hopefully see you next year.  Welcome 2014 !

Albert, 20-12-2013.

 

How to Measure Full Well Capacity (3)

December 6th, 2013

From the two foregoing discussions on the full well capacity, it could be learned that :

-       In the case the full well is determined/limited by the ADC, comparable results for the FWC can be obtained by means of linearity measurements as well as from the mean-variance method,

-       In the case the full well is not determined/limited by the ADC, the results obtained from the linearity measurements show larger full well values than the ones obtained from the mean-variance method.

To explain the discrepancy between the FWC data of the latter case, one should realize that when the average output signal turns into saturation, a few non-uniformity issues are simultaneously popping up :

-       PRNU or photo-response non-uniformities : the pixels with the highest sensitivity can reach saturation first,

-       Non-uniformities in saturation level, some pixels will saturate at a lower FWC than others,

-       It is not clear from the measurements which part of the pixel is causing the saturation : the pinned-photodiode, the floating diffusion capacitance, the output swing limitation of the source-follower, output limitation swing of the analog circuitry.  Moreover, all these limitations can interfere with each other, which makes the situation even more complex to understand and explain.

To find out what is going on, the fixed-pattern noise is measured, and some interesting results were obtained.  The analog gain is put to a low value, and the reference voltage of the ADC is set to a higher voltage (the reference voltage is defining the analog input voltage that corresponds to an output of all “1”s).  In this way the ADC is not limiting the output swing, neither defining the FWC.

The measurement results are shown in the figure 1 : the left axis indicates the average output signal of 100 x 100 pixels as a function of the integration/exposure time; the right axis shows the fixed-pattern noise obtained from these 100 x 100 pixels, also as a function of the exposure time.

Figure 1 : Average sensor output and fixed pattern noise as a function of exposure time for a window of 100 x 100 pixels.

Some interesting details can be revealed from the FPN data :

-       For very low values of the signal (exposure time < 1 ms), the FPN shows a kind of plateau, indicating the FPN in dark,

-       For moderate values of the signal (1 ms < exposure time <12 ms), the FPN linearly increases, determined to the PRNU, the latter is proportional to the average signal value,

-       For higher values of the signal, in the region where the output signal tends to saturate (12 ms < exposure time < 16 ms), the FPN grows faster and tends to saturate as well.  Most probably this is the effect of the pixels that saturate.  The FPN at saturation is larger than the PRNU and for that reason the FPN increases.  The FPN tends to saturate, because once all pixels are saturated, the FPN does no longer change,

-       For saturated values of the signal (16 ms < exposure time < 20 ms) the FPN gets a second boost.  It is not completely clear what is happening here (the camera and sensor are “unknown”), but most likely the double sampling of the reference and useful signal start showing some “black sun” or “eclipse” effects.  This results in a larger FPN,

-       For the largest exposure times (exposure time > 16 ms), all pixels are running in the “black sun” or “eclipse” mode, but apparently the sensor is provided with an anti-eclipse circuit which pins the column voltages to a fixed voltages.

The abovementioned explanation is based on a close observation of what the behavior of the output signal.  This is illustrated in Figure 2, showing the same results as the ones mentioned in Figure 1, but with an adapted scale on the vertical axis.

Figure 2 : Same data as shown in Figure 1, but with an adapted scale on the left vertical axis.

As can be noticed, the average output signal tends to reach saturation for an exposure time of (about) 17 ms, but then the average output signal decreases again for a longer exposure time.  From 20 ms onwards, the average output signal seems to be clipped to a particular value, so does the FPN.  A simple explanation for this effect can be the presence of an anti-eclipse circuit.

Anyone else has a better explanation ?

Albert, 06-12-2013.

Forum ADC’s for Imagers is completely SOLD OUT !

December 4th, 2013

The two planned sessions on Dec. 16-17, and Dec. 19-20, 2013 are completely sold out.  There is no need for further regsitration because more seats will not be added.  Thanks to all people who registered.  I will keep you updated about the feedback of the participants.  At that time I will also start with the preparation of a new forum in 2014.

Albert, 4-12-2013.

Status Imaging Forum “ADCs for Imagers”

November 15th, 2013

I just want to give the imaging community a quick update on the registration situation for the forum “ADC’s for Imagers”.

Because the interest was/is much higher than expected, a second session will be organized (this was already announced earlier), and the number of seats is each session is slightly increased (from 24 to 32).

At this moment registration for the forum is still possible, because :

- for the session on Dec. 16 & 17, 2013, there are still 2 seats left,

- for the session on Dec. 19 & 20, 2013, there are still 3 seats left.

If anyone is still interested for registering, take your chance !  Keep in mind that in 2014 the forum will be organized again, but with a different subject !

 

Albert, 15-11-2013.

How to Measure Full Well Capacity (2)

November 4th, 2013

In the previous blog, the measurement of the full well capacity (FWC) was explained based on the measurement of the output signal versus the input signal.  The input signal was generated by a constant light source in combination with a varying exposure time.  But once all output data is available, not only the average value of the pixels can be calculated but also the temporal noise level for every pixel.  This will be done in this blog.

From the discussion of the photon-transfer curve and its properties, it was learned that when the photon shot noise is the dominant noise source, the following formula can be written down :

ntemp2 = k×(Sout - Soff)

with :

ntemp : the total temporal noise on pixel level,

k : conversion gain,

Sout : the average signal on pixel level,

Soff : offset value, or the average signal for 0 s exposure time.

(Normally I use a sigma-symbol for the noise, but the bloody software does not accept the sigma-symbol.)

So instead of looking after the saturation level of the signal, one can also look after the saturation or the peak level in noise and try to calculate the FWC based on the noise measurements.  The FWC is defined at the point at which the temporal noise reaches its maximum value.

1)    Saturation of the sensor is larger than maximum value of the ADC.

In such a case, most of the time the maximum value of the sensor or camera ADC is set such that the complete ADC range covers the linear part of the sensor’s output response.  An example of a camera in which the ADC defines the maximum output level of the system is shown in Figure 1, where the sensor noise variance is shown as a function of the exposure time (the data already collected in the previous blog is reused here).

Figure 1 : Noise variance as a function of exposure time, under a constant illumination level.

Shown is the temporal noise variance as a function of the exposure time at a constant illumination level (the exact value of the light input is not important for this measurement, as long as it stays constant).  As can be observed, the transition from a monotonically increasing output value of the variance to zero goes pretty abruptly.  This is a clear indication that the ADC defines the saturation level.  Moreover, the peak value of the temporal noise variance is equal to 194,600 DN.

For this example, the definition of the full well capacity is equal to the variance peak value divided by k, minus the offset  value of the noise variance at 0 s exposure time divided by k, or (194,600/k) – (7667/k) = 63,887 – 2517 = 61,370 DN.

Taking into account the conversion gain of the sensor (3.046 DN/electron, for the TIFF format it is 64x larger than what can be measured at the output of the sensor), this results in a FWC = 20,148 electrons.

2)    Saturation of the sensor is smaller than maximum value of the ADC.

In this case, the FWC needs a clear definition : is FWC referring to the saturation level of the sensor/camera, or is FWC referring to the maximum linear part of the sensor’s output swing ?  The former can be referred to a FWCsat, while the latter can be indicated by FWClin.  But now the question arises : how to define the linear part of the sensor’s output swing ?  In the previous blog, FWClin was set at the point where the sensor’s output deviated maximum 3 % of the linear behavior.  Taking that definition and transferring it to the noise variance measurement, now FWClin will be defined at the point where the noise variance deviates maximum k×(3 %) = 4.5 %.

An example of a camera in which the ADC maximum output value is larger than the saturation level of the sensor is shown in Figure 2.

Figure 2 : Noise variance as a function of exposure time, under a constant illumination level.

 

Shown is the temporal noise variance as a function of the exposure time with a constant illumination level (the exact value of the light input is not important for this measurement, as long as it stays constant).  As can be observed, the transition from a monotonically increasing output value to a decrease of the noise variance goes smoothly.  This is a clear indication that the ADC is NOT defining the saturation level of the system.

For this example, the definition of the full well capacity at saturation is equal to the maximum level of the noise variance divided by k, minus the offset of the noise variance measured at 0 s exposure time and also divided by k, or (41,260/k) – (2389/k) = 27,013 DN.  Taking into account the conversion gain of the sensor (1.493 DN/electron), this results in a FWCsat = 18,093 electrons.

 

But as mentioned before, this is only half of the story, because the sensor’s response is very nonlinear close to saturation.  For that reason the linearity (INL) of the sensor is characterized and plotted in Figure 2 as well.  At the point where the real output characteristic deviates 4.5 % from its regression line, the FWClin is defined.  In this example, the following number can be found : (39,780/k) – (2389/k) = 25,044 DN, translating in FWClin = 16,774 electrons.

It should be clear that this last number is very much depending on the definition of FWClin.  If the 4.5 % deviation is translated in 1.5 %, the value for the FWClin will become smaller, or if the 4.5 % deviation is translated in 7.5 %, the opposite becomes valid.

 

Note : the data shown in Figures 1 and 2 are obtained from the same sensor, with the same light input.  The difference between the two measurements is a difference in camera setting, such that the analog gain of the sensor and the reference voltage of the ADC result in an overall camera gain difference of a factor of 2.

Explained in this blog is the measurement of FWC based on noise variance.  Again it can be learned that the values obtained for the FWC strongly depend on the exact definition of the full well capacity.  Lesson to take away : if the FWC is specified in an image sensor’s datasheet, first ask yourself “How is the FWC defined ?”.

See you next time !

Albert, 04-11-2013.

Post-Doc Opening at TU Delft

October 13th, 2013

On a very short notice, I will start with a couple of new projects at the TU Delft.  The main emphasis of the projects will be ultra-low noise CMOS image sensors.  A post-doc position is open for a project leader for these projects.  Preferably, the candidate for this position has a background (=PhD degree) in solid-state image sensing and/or mixed signal-design.  Those who might be interested can directly contact me : a.j.p.theuwissen at tudelft.nl, and it would be very helpful if you can send me your resume right away.

Albert, 13-10-2013.

How to Measure Full Well Capacity (1)

September 27th, 2013

The next parameter to be characterized is the full well capacity of the sensor.  But before any measurement or characterization can be done it is important to make clear what is the definition of the full well capacity (FWC).  To come to that point, let’s treat two different situations, the first one in which the ADC is setting the saturation of the sensor and the second one in which the ADC is not setting the saturation of the sensor.

1)    Saturation of the sensor is larger than maximum value of the ADC.  In such a case, most of time the camera/sensor designer is setting the maximum value of the ADC such that the complete ADC range covers the linear part of the sensor’s output response.  An example of a camera in which the ADC defines the maximum output level of the system is shown in Figure 1.

                       

Figure 1 : Sensor output value as a function of exposure time, under a constant illumination level.

Shown is the sensor output as a function of the exposure time with a constant illumination level (at this stage of the discussion, the exact value of the light input is not important for this measurement, as long as it stays constant, so that various setting of the camera and/or sensor can be compared with each other).  On the right axis the integral non-linearity is shown as well.  As can be observed, the transition from a monotonically increasing output value to saturation goes pretty abruptly.  This is a clear indication that the ADC defines the saturation level.  Moreover, the value of the saturated output is equal to 216 – 1 = 65535 DN (216 is coming from the TIFF format).

For this example, the definition of the full well capacity is equal to the saturation level (of the ADC) minus the offset at zero seconds exposure time, or 65535 – 2106 = 63429 DN.  Taking into account the conversion gain of the sensor (3 DN/electron, for the TIFF format it is 64x larger than what can be measured at the output of the sensor), this results in a FWC = 19820 electrons.

2)    Saturation of the sensor is smaller than maximum value of the ADC.  In this case, the FWC needs a clear definition : is FWC referring to the saturation level of the sensor/camera, or is FWC referring to the maximum linear part of the sensor’s output swing ?  The former can be referred to a FWCsat, while the latter can be indicated by FWClin.  But then the question arises : how to define the linear part of the sensor’s output swing ?  Very often, FWClin is defined at the point where the deviation of the sensor’s output and an ideal straight line is maximum 3 %, or at the point where the sensor’s output is linear up to 97 % or better.  An example of a camera in which the ADC maximum output value is larger than the saturation level of the sensor is shown in Figure 2.

Figure 2 : Sensor output value as a function of exposure time, under a constant illumination level.

 

Shown on the left vertical axis is the sensor output as a function of the exposure time with a constant illumination level (the exact value of the light input is not important for this measurement, as long as it stays constant), shown on the right vertical axis is the corresponding integral non-linearity (INL).  As can be observed, the transition from a monotonically increasing output value to saturation goes smoothly.  This is a clear indication that the ADC is NOT defining the saturation level of the system.

For this example, the definition of the full well capacity at saturation is equal to the saturation level minus the offset at zero seconds exposure time, or 51880 – 1602 = 50278 DN.  Taking into account the conversion gain of the sensor (1.5 DN/electron), this results in a FWCsat = 33518 electrons.

But as mentioned before, this is only half of the story, because the sensor’s response is very nonlinear close to saturation.  For that reason the linearity (INL) of the sensor is characterized and plotted in Figure 2 as well.  At the point where the real output characteristic deviates 3 % from its regression line, the FWClin is defined.  In this example, the following number can be found : 45860 – 1602 = 44258 DN, translating in FWClin = 29505 electrons.

It should be clear that this last number is very much depending on the definition of FWClin.  If the 3 % deviation is shifted to 1 %, the value for the FWClin will become smaller, or if the 3 % deviation is shifted to 5 %, the opposite becomes valid.

 

Note : the data shown in Figures 1 and 2 are obtained from the same sensor, with the same light input.  The difference between the two measurements is a difference in camera setting, such that the analog gain of the sensor and the reference voltage of the ADC result in an overall camera gain difference of a factor of 2.

Explained in this blog is the measurement of FWC based on linearity measurements.  Again it can be learned that the values obtained for the FWC strongly depend on the exact definition of the full well capacity.  Lesson to take away : if the FWC is specified in an image sensor’s datasheet, first ask yourself “How is the FWC defined ?”.

See you next time !

Albert, 27-09-2013.

Playing Time (3)

September 9th, 2013

Once more thanks for all the reactions.

I checked the reactions again this morning, and it is clear that the right answer/suggestion came from David San Segundo Bello (imec).  Already in one of the very first reactions, he mentioned a possible drift of the LED light source due to an AC variation on top of the DC voltage.  Afterwards Guy Meynants (CMOSIS) repeated the answer of David, but also added to it the method to check it out, namely by means of noise measurements.  That actually completed the story.  So I think it is fair to give both guys a bottle of wine.

Albert, 09-09-2013.