Harvest Imaging Blog

The ISSCC forum, organized on Thursday, was focusing on Noise in Sensors (very general).  A total of 9 presentations were given, of which (only) 3 focused on Imagers.  The undersigned opened the forum with a general overview of Noise in Image Sensors, in the early afternoon Shoji Kawahito give a presentation on Low-Noise Image Sensors, and to conclude the forum, Neale Dutton had a talk about Noise in Single-Photon Detectors. 

On one hand many people appreciated the general overview of noise present in many different type of sensors, on the other hand, not that many imaging engineers attended the forum because of the low number of talks about imagers.  Nevertheless, the ISSCC organization seemed to be pretty happy with the number of registrations. 

One very interesting detail from Neale’s presentation : he showed a very nice graph of published noise data which I include here in this blog (with permission of Neale !).  On the vertical axis the input referred read noise in electrons is shown versus the conversion gain of the pixels.

The three lines shown in the graph are lines of equal read noise, “equinoise” lines, but this time noise expressed in uV.  As can be seen, the lowest noise ever reported was 0.22 electrons, presented in JEDS2015, but the lowest noise ever reported in the voltage domain was 30 uV, presented at ISSCC2012.  I do know that expressing noise in equivalent number of electrons is a very common technique which I support as well, but nevertheless, looking to the noise in the good, old classical way gives a complete other image.  Now the challenge is to keep the 30 uV of noise level alive, while increasing the conversion gain !

Thanks Neale (not Neil) for this “fresh” view on the noise !

Albert, 08-02-2016.

In this third and last review of the ISSCC, 2 remaining imaging papers are left.

The first one comes from NHK, and deals with a 1.1 um 33 Mpixel device, 3D stacked and 3-stage cyclic-based ADC.  This 3D stacking is realized by means of direct bonding (in the columns).  TSV’s are avoided because they seem to be too expensive, they cost more masks, they consume area and they make a more complicated lay-out needed.  The device is fabricated at TSMC (at least TSMC is mentioned in the acknowledgement), and to my knowledge this is the first CMOS image sensor made in 45 nm 1P4M.  The logic part on the second level of silicon is made in 65 nm 1P5M technology.

The ADC implemented on the chip (by Shizuoka Univ./Brookman Technology) is a three stage design, the first two stages are cyclic ADCs (upper 3 bits and middle 6 bits), the last stage is  a SAR ADC (3 bits).  The sensor can run at full resolution (33 Mpixels !) at a rate of 240 fps, burning 3 Watts.

The last paper from the imaging session is the one that was published by FBK, Trento, with two brothers as authors (does not happen that often).  The device presented is intended for spacecraft navigation and landing.  It contains 64 x 64 pixel digital silicon photomultiplier direct ToF with 100 Mphotons/s/pixel background rejection.  Every pixel (out of the 64 x 64 array) contains 8 SPADs  with extra electronic circuitry.  The pixel is designed such that uncorrelated photons or dark current (which still trigger the SPADs) do not give an output from the pixel.  Only correlated photons give an output.  So the background suppression and dark count suppression is more or less based on the statistics of these signals (compared to the ToF signal), and is implemented in the digital logic within every pixel.  Fabrication technology is 150 nm CMOS with 6 metal layers.  Pixel fill-factor is 26.5 %.

Albert, 08-02-2016.

At ISSCC several high resolution imagers were presented.  Champion was the device of CMOSIS with 391 Mpixel device for airborne mapping applications.  The device itself is pretty straight forward with 3.9 um pixel pitch, 4T non shared pixels, 31.5 ke^– full well, 45 uV/e^– conversion gain, 3.7 e^– of noise at unity gain, resulting in 78 dB dynamic range.  The 14-bit SS ADCs are placed in the columns and are located at the two sides of the imager.  So the pitch of the ADCs is 7.8 um.  Jan Bogaerts showed impressive images of the device, and after the “show” I had the opportunity to take a look at a real device in its package.  The sensor is using stitching : 6 x 3 blocks are stitched in the active area, with 4352 x 5000  pixels in each stitched block.  Processing was done at ST in a 90 nm FE/65 nm BE process 1P4M.  The device is monochrome, but in the final application, this monochrome sensor is surrounded by CCDs that provide the colour information.  During Q&A it was mentioned that the camera is using a mechanical forward motion compensation technique to compensate for the movement of the camera during exposure.

During the presentation Jan Bogaerts made a comparison with a CCD of the competition.  Amongst several characteristics, he mentioned that his CMOS sensor is free of smear in contradiction to the CCD.  In a private discussion afterwards with Jan Bogaerts he told me that the camera is using a mechanical shutter (that is no secret I guess), but one should realize that in that case a CCD is neither showing any smear issues.

Hirofumi Totsuka of Canon presented a 250 Mpixel APS-H size imager : 1.5 um pixel pitch (4 sharing) made in 0.13 um technology node.   The device is consuming 1.97 W at full resolution 5fps.  An interesting build-in feature of this sensor is the following :  ALL pixel signals are converted by column SS-ADCs with a single ramp, but in front of the ADC, each column has its own PGA that can be switched to 4x or 1x gain, depending on the signal level.  So when the pixels are sampled, a first check is done to look whether the signal is above or below a particular reference level, and then the right gain of the PGA is set to 1x or 4x.  Simple method, but I think that the issues pop up in the reconstruction of the signal at the cross-over point between the two settings of the PGA.

 

Kei Shiraishi of Toshiba presented a stacked sensor with 1.2 e^– of noise with a comparator-based multiple sampling PGA.  The most important characteristic is the multiple sampling in the analog domain.  This goes much faster than the multiple sampling in the digital domain.  After 32 samples of each signal, a noise level of 1.2 e^– could be reached for 1 M pixels at 20 fps.  The device is realized in 65 nm, both for the sensor as well as for the circuit on the second silicon level.  It was mentioned in the paper, but I guess that the noise floor without the multiple sampling should be around 5 e^– at 30 fps, going down to the 1.2 e^– reported at 20 fps.

Charles Liu of TSMC showed the results obtained by a 33 Mpixel, stacked device with a negative substrate bias.  The idea is actually pretty simple, maybe the implementation is more complicated. “Simply” bias the substrate of the sensor to -1.3 V and you can lower all other supply voltages by 1.3 V.  So instead of having a power supply of 3.3 V, the device has now a supply of 2.0 V.  But the large pixel swing is maintained by means of the negative substrate bias.  The sensor is fabricated in 65 nm 1P5M technology.

Albert, 05-02-2016.

Already quite a bit of words are spent on the organic conductive sensors presented in the Panasonic papers.  Nevertheless, here is some more info.

Kazuko Nishimura presented the paper on the large HDR sensor with a low noise level.  A few remarks about this sensor :

–          HDR is obtained by two light sensitive areas within one pixel : one with low and one with high sensitivity.  This is a very similar method as proposed long time ago by Fuji in their SuperCCD,

–          The pixels do suffer from kTC noise, but by means of a cleaver circuit/feedback, they are able to reduce the remaining kTC noise to 1.2 e^– reset noise and to 5.4 e^– overall.  In combination with a full well of 600 ke^–, it creates a gorgeous dynamic range,

–          The process used to fabricate the sensor is 65 nm CMOS, 1P3Cu1Al,

–          The results mentioned are overall not bad, but there was no information provided about dark current, about quantum efficiency, about uniformity and about reliability of the material.  So this suggests (to me) that there are still some issues to solve.

Sanshiro Shishido presented the paper on the global shutter version of the organic photoconductor sensor.  The topplate of the photoconductor is made out of ITO and needs to be biased to larger voltages.  But the overall light sensitivity of the organic photoconductor depends strongly on the exact voltage on the ITO gate.  A lower voltage on the ITO gate lowers the light sensitivity and actually 0 V on the gate makes the sensor even blind.  In this way one can create a global shutter functionality to the sensor.  Moreover, one has the possibility to modulate the sensitivity during the exposure time, for instance, the exposure time can be split in parts in which the sensor will be sensitive and in parts in which the sensor will be insensitive.  Even one has the option to modulate the sensitivity during the periods the sensor is sensitive by means of adapting the high voltage set to the ITO gate.  Overall a nice technology !

The third paper of Panasonic, presented by Yoshihisa Kato had nothing to do with the organic conductive layer, but the sensor presented was provided with an EM-function.  The latter is built in the vertical direction of the silicon.  This is new and is never shown before in an imager (to my knowledge).  The EM-functionality can be switched on and off by means of the voltage biasing the substrate (around 23 V).  Amazing images were shown (shot at extremely low light levels).  From the data shown, it looks like the EM is very strongly depending on the exact voltage on the substrate.  In comparison to the well-known EM-CCD and EM-CMOS devices (which are on the market), the EM-multiplication in the latter is done with very small gain steps, but by doing multiple EM-steps finally a large gain can be reached.  In the case of the Panasonic paper, the EM is done only once, so all the gain needs to be created in one step.  Has this way of working advantages or disadvantages compared to EM-CCD and EM-CMOS ?

Albert, 04-02-2016.

More or less by coincidence I recently visited the website of Springer and I found out that the book I wrote in 1995, entitled “Solid-state imaging with charge-coupled devices”, is still available.  But the price is incredibly, unreasonably high.  They charge 390 Euro for the book.  This is ridiculous !!!!  I am writing this blog just to mention that I absolutely have no influence on the price setting.  With this high price apparently Springer simply tells to their customers that they preferably do not sell the book anymore.

Originally the book was published by Kluwer, and even in the early days, also Kluwer was charging quite a bit of money.  If I remember well it was around 200 Dutch Guilders, equivalent to 100 Euro.  20 years ago this was also a lot of money.  But nevertheless, the book was selling quite well, and 100 Euro is still a lot less than 390 Euro.  Unfortunately I do not have that many copies left (only 4) of the book, otherwise I could start my own little business with selling my own book for an acceptable price …

If Springer is not ashamed of this price setting, at least I am ….

Message to potential new authors of technical books : you do not become rich of writing a book, but someone else will !!!

Albert, 19-01-2016.

Over the last couple of weeks extra measurments were performed to better understand the working and limitations of the Phase Detection Auto Focus Pixels.  The extra measurements focused on :

– the influence of the exposure time on the PDAF pixel signals and the possibility to extract useful focusing information from it,

– angular light dependency of the PDAF pixels.

The new measurements are included in an update version of the report.  The full report is still available through info (at) harvestimaging (dot) com.

Below you find the table of contents of the updated version of the report.

15-01-2016.

Table of Contents

 

List of Figures

Introduction

Working principle of PDAF pixels

Theoretical implementation of PDAF pixels

Practical implementation of PDAF pixels

From the theory to the reality

Measurement 1 : influence of F-number

Measurement 2 : influence of the object distance

Measurement 3 : influence of the object angle

Measurement 4 : influence of the PDAF location on the sensor

Measurement 5 : influence of the object colour

Measurement 6 : Influence of exposure time

Conclusions

Appendix : angular dependency of the PDAF pixel sensitivity

Acknowledgement

References

 

List of Figures

 

Figure 1. Imaging with a positive lens

Figure 2. Requirement to have an image in-focus at the surface of the image sensor

Figure 3. illustration of rear focus, in-focus and front focus

Figure 4. Illustration of two different rear focus situations

Figure 5. Illustration of two different front focus situations

Figure 6. Optical ray formation from the object till the photodiode of an image sensor

Figure 7. Optical ray formation from the object till the partly optically-shielded photodiodes/pixels of an image sensor

Figure 8. Aptina’s MT9J007C1HS architecture with 9 rows containing auto-focus pixels based on phase detection

Figure 9. Microphotograph of one of the AF rows

Figure 10. Magnified view of an AF row

Figure 11. Microphotograph of an AF row

Figure 12. Sensor architecture indicating the various AF lines as well as the different zones used to read the sensor

Figure 13. Image taken from a random scenery with the AF option switched ON

Figure 14. Analysis of the signals of AF-line 5 in zone 5

Figure 15. Image taken from the same scenery as in Figure 13 with the AF option switched OFF and manually focused on the “macro” position

Figure 16. Analysis of the signal of AF-line 5 in zone 5 in the case the AF system is forced to “macro” position

Figure 17. Image taken from the same scenery as in Figure 13 with the AF option switched OFF, and manually focused on the “infinity” position

Figure 18. Analysis of the signals of AF-line 5 in zone 5 in the case the AF system is forced to “infinity” position

Figure 19. Odd and even PDAF signal for an object place 50 cm in front of the camera and the lens switched to auto-focus “ON”

Figure 20. Odd and even PDAF signal for an object placed 50 cm in front of the camera and the lens focusing on “infinity”

Figure 21. Odd and even PDAF signal for an object place 50 cm in front of the camera and the lens focusing on “macro”

Figure 22. Depth-of-field as a function of the object distance for 3 F-numbers, the dotted lines indicate the corresponding hyper-focal distances

Figure 23 PDAF pixel shift as a function of F-number for an object 50 cm in front of the camera and auto focusing

Figure 24 PDAF pixel shift as a function of F-number for an object 50 cm in front of the camera and focusing at “infinity”

Figure 25 PDAF pixel shift as a function of F-number for an object 50 cm in front of the camera and focusing at “macro”

Figure 26 Front-focus situation

Figure 27. PDAF pixel shift as a function of object distance and auto-focus setting of the camera, with F2.8

Figure 28. PDAF pixel shift as a function of object distance and auto-focus setting of the camera, with F5.6

Figure 29. PDAF pixel shift as a function of object distance and auto-focus setting of the camera, with F11

Figure 30. PDAF pixel shift as a function of object distance, lens auto-focus setting on “infinity” and with F2.8

Figure 31. PDAF pixel shift as a function of object distance, lens auto-focus setting on “infinity” and with F5.6

Figure 32. PDAF pixel shift as a function of object distance, lens auto-focus setting on “infinity” and with F11

Figure 33. PDAF pixel shift as a function of object distance, lens auto-focus setting on “macro” and with F2.8

Figure 34. PDAF pixel shift as a function of object distance, lens auto-focus setting on “macro” and with F5.6

Figure 35. PDAF pixel shift as a function of object distance, lens auto-focus setting on “macro” and with F11

Figure 36. PDAF pixel shift as a function of object distance, lens focusing fixed at 60 cm and F2.8

Figure 37. PDAF pixel shift as a function of object distance, lens focusing fixed at 60 cm and F5.6

Figure 38. PDAF pixel shift as a function of object distance, lens focusing fixed at 60 cm and F11

Figure 39. PDAF pixel shift as a function of object angle and auto-focus setting of the camera, with F2.8

Figure 40. PDAF pixel shift as a function of object angle and auto-focus setting of the camera, with F11

Figure 41. PDAF pixel shift as a function of object angle, lens focusing on “infinity” and with F2.8

Figure 42. PDAF pixel shift as a function of object angle, lens focusing on “infinity” and with F11

Figure 43. PDAF pixel shift as a function of object angle, lens focusing on “macro” and with F2.8

Figure 44. PDAF pixel shift as a function of object angle, lens focusing on “macro” and with F11

Figure 45. PDAF pixel shift as a function of the PDAF location in readout zone 5 and auto-focus setting of the camera, with F2.8

Figure 46. PDAF pixel shift as a function of the PDAF location in readout zone 5 and auto-focus setting of the camera, with F11

Figure 47. PDAF pixel shift as a function of the PDAF location in readout zone 5 and lens focusing on “infinity” and with F2.8

Figure 48. PDAF pixel shift as a function of the PDAF location in readout zone 5 and lens focusing on “infinity” and with F11

Figure 49. PDAF pixel shift as a function of the PDAF location in readout zone 5 and lens focusing on “macro” and with F2.8

Figure 50. PDAF pixel shift as a function of the PDAF location in readout zone 5 and lens focusing on “macro” and with F11

Figure 51. Location of the various PDAF regions under test

Figure 52. PDAF pixel shift as a function of the PDAF location on AF-line 5 and on the diagonal with auto-focus setting of the camera and F2.8

Figure 53. PDAF pixel shift as a function of the PDAF location on AF-line 5 and on the diagonal with auto-focus setting of the camera and F11

Figure 54. PDAF pixel shift as a function of the PDAF location on AF-line 5 and on the diagonal with focus set to “infinity” and F2.8

Figure 55. PDAF pixel shift as a function of the PDAF location on AF-line 5 and on the diagonal with focus set to “infinity” and F11

Figure 56. PDAF pixel shift as a function of the PDAF location on AF-line 5 and on the diagonal with focus set to “macro” and F2.8

Figure 57. PDAF pixel shift as a function of the PDAF location on AF-line 5 and on the diagonal with focus set to “macro” and F11

Figure 58. Pulses used to measure the PDAF pulse shifts in zones 2 to 5 on the AF-line 5, lens focus at “infinity” and F2.8

Figure 59. Incoming rays for a PDAF pair at the edge of the sensor

Figure 60. Pulses used to measure the PDAF pulse shifts in zones 2 to 5 on the AF-line 5, lens focus at “macro” and F11

Figure 61. Odd and even pulses in AF-line 5, in AF-line 5 + 1 line and in AF-line 5 + 2 lines, with green light input, F2.8 and focusing at “infinity”

Figure 62. Odd and even pulses in AF-line 5, in AF-line 5 + 1 line and in AF-line 5 + 2 lines, with green light input, F2.8 and focusing at “infinity”

Figure 63. Odd and even pulses in AF-line 5, in AF-line 5 + 1 line and in AF-line 5 + 2 lines, with green light input, F2.8 and focusing at “infinity”

Figure 64. PDAF pixel shift as a function of object colour and auto-focus setting of the camera, with F2.8

Figure 65. PDAF pixel shift as a function of object colour and auto-focus setting of the camera, with F11

Figure 66. PDAF pixel shift as a function of object colour, lens focusing on “infinity” and with F2.8

Figure 67. PDAF pixel shift as a function of object colour, lens focusing on “infinity” and with F11

Figure 68. PDAF pixel shift as a function of object colour, lens focusing on “macro” and with F2.8

Figure 69. PDAF pixel shift as a function of object colour, lens focusing on “macro” and with F11

Figure 70. PDAF pixel shift as a function of exposure time, with F2.8, focusing on “macro” and white light input

Figure 71. PDAF pixel shift as a function of exposure time, with F2.8, focusing on “macro” and green light input

Figure 72. PDAF pixel shift as a function of exposure time, with F2.8, focusing on “macro” and blue light input

Figure 73. PDAF pixel shift as a function of exposure time, with F2.8, focusing on “macro” and red light input

Figure 74. Angular dependency of the PDAF pixels under the influence of white light

Figure 75. Angular dependency of the PDAF pixels (corrected data) in combination with the sum of the PDAF pixel signals

Figure 76. Angular dependency of the PDAF pixels (corrected data) in combination with the green pixels signals from neighbouring red-green and blue-green rows

 

Best Wishes for a successful 2016 !

Stay tuned !  Within a few days from now , Harvest Imaging will launch its new website.

Albert.

Good Bye 2015 ! 

At the very end of 2015, it is good to take a look backwards to see what 2015 has brought.

Actually I can start with the same statement as last year : “It was a busy year for Harvest Imaging”.  Several imaging trainings were organized, in-house as well as public courses.  Thanks to CEI, FSRM and Framos who organized the public or open courses.  Thanks to all my customers for the in-house courses.   Thanks to all participants for being there, because without participants there will be no trainings !  And apparently the imaging business is doing very well, because a lot of companies have hired new imaging engineers and consequently they are again asking for more trainings in 2016.  That is of course the best feedback I can get : returning customers. 

The Harvest Imaging Forum 2015 was targeting “3D Imaging with ToF”.  I was very happy to get the possibility to attract David Stoppa as the speaker for this forum.  The two sessions in December got very good feedback from the participants, and in January 2016 a third (and last) session will be organized.  The only drawback : the expectations for the third session are high, so David knows what to do.  At this moment I can announce already that also n 2016 another Harvest Imaging forum will be organized.  Topic and speaker(s) are still to be defined.

In 2015 Harvest Imaging came with a new product on the market : a reverse engineering report of a particular imaging feature present in a commercial camera.  The first reverse engineering report was devoted to Phase Detection Auto-Focus Pixels.  This is a very interesting topic, very well described in patents of several Japanese camera manufacturers, but the technical results obtained by these PDAF pixels are hardly described in public literature.  For that reason Harvest Imaging started working on it.  At the end of the road, a nice report is ready and is for sale.  And one of the conclusions is that it took more time and effort than originally planned.  (This is in line with the 3^rd law of Hofstadter : A project always takes longer than originally scheduled, even if you take the 3^rd law of Hofstadter into account).  But it still was worthwhile doing it.  We learned a lot from the PDAF measurements.  And if a “good” subject pops up in 2016, a new reverse engineering effort will start.

If I look back to the activities that took place in relation to the imaging society (IISS), then Harvest Imaging took a very active role in the organization of the 2015 International Image Sensor Workshop.  In close cooperation with imaging peers (Johannes Solhusvik and Pierre Magnan) a successful workshop was organized.   About 100 technical papers were presented during almost 4 days.  But also remarkable : during the social activity of the workshop, all 180 participants went out for biking in an old cave.  Their reactions afterwards were quite interesting : most of them enjoyed it very much, a few much less.

As you can read, 2015 was very busy and was interesting because it was so diverse.  I do hope that the customers of Harvest Imaging can close 2015 with a big smile on their face.  I would to thank all of them for the business in 2015 and I am looking forward to serve them again in 2016.

Welcome 2016 !  Looking forward to another successful year, although without an IISW, but with a new special issue of IEEE Transactions on Electron Devices on Image Sensors coming up !

Wishing all my readers a Merry Christmas and a Happy New Year.  “See” you soon.

Albert, 23-12-2015.

Time-Delayed Integration or TDI in CMOS seems to be a hot topic (at least for space applications), but it also still is a challenging architecture to build in CMOS technology.  At the CNES CMOS image sensor workshop in Toulouse (held about 10 days ago), there were several presentations on CMOS-TDI, here is an overview.

C. Virmentois presented the CNES work on TDI.  They finished several projects with ESPROS (CCD on CMOS), IMEC (CCD on CMOS) and ST (digital CMOS TDI).  At this moment work is going on in the field of a multi-spectral TDI with large pixels, also this latest device is based on a digital TDI.

W.r.t. to the chip(s) made at ESPROS, the following details were given :

• Fully depleted, BSI,
• 7.5 um and 6.5 um pixel pitch for monochrome,
• 26 um and 52 um pixel pitch for multi-spectral,
• noise level of 600 uV,
• dark current of 2.6 nA/cm2 at 20 ^oC,
• conversion gain : 10… 15 uV/e^–,
• CTI : 2.10^-3,
• INL < 1.5 %,
• FWC : 92 ke^– at 1 V,
• QE > 70 % n-IR.

A second chip made at ESPROS showed improved results, such as :

• Noise : 350 uV,
• Dark current : 1ke^–/s (= 10 x less),
• CTI : … 1.10^-4 …

During the presentation it was not mentioned which part of the processing was done by ESPROS and/or which part of the processing was done by a third party.

 

M.-Y. Yeh of NAR Labs reported the work on TDI done in his lab :

• 6 lines, 2 PAN + 4 multi-spectrum,
• 7.5 um pixel pitch for PAN and 30 um pixel pitch for multi-spectrum,
• Based on 4T BSI 2.5 um pixels, made in TSMC 0.11 um process,
• Stitched with 8 blocks next to each other, chip width : 12.288 cm.

 

F. Mayer of e2v mentioned that the first TDI made by his company was already done in 2010 with charge transfer in a 0.18 um CMOS process.  Later more CCD-like devices were made.

For the digital domain, Frederic mentions :

• Too much load for the ADC,
• Motion MTF issues,
• Dynamic range is OK, but the noise is pretty high.

For the charge domain :

• Limitation in full-well capacity.

The combination of digital and charge domain can overcome a number of drawbacks, but the architecture will be pretty complex.

The first generation charge transfer TDI was built on a surface channel CCD, the second generation was provided with a buried channel.

Neither in this presentation the fab was mentioned that fabricated the CMOS wafers.

 

Ben-Ari of SemiConductors Devices gave a large list of performance data of the TDI’s made by his company.

In summary :

• 4 independent TDI arrays,
• digital running TDI with global shutter,
• 0.18 um technology,
• Chip size : 84 x 16 mm², 2600 pixels x 8 to 64 pixels,
• Full well : 300 ke^–, < 80 e^– noise, and 72 dB dynamic range,
• 50 … 10,000 lines/s,
• Dark current < 400 e^–/s at 25 ^oC,
• Single slope ADC,
• Stitched in 1 dimension,

Current status of these devices : BSI delivered, wafer sort done with good yield.

 

Boulenc gave an overview of IMEC’s TDI status :

• 0.13 um, CMOS flow with 3.3 V and 1.5 V power supply,
• Generation 1 (see also CNES presentation) with lateral AB and dedicated implants at the output to make it BSI compatible,
• Generation 2 : 5 um pixel size, 1025 x 512 pixels, gate spacing between 100 nm and 180 nm, CF : 25 uV/e^–, 2.5 nA/cm² dark current at 25 ^oC, 0.5 mV noise floor and 17 ke^– full well capacity,
• Generation 3 is in development.

 

In conclusion : a lot of interesting work is going on in the field of TDI-CMOS, but apparently none of the developments has yet resulted in commercially available devices with a performance that matches the existing TDI-CCD performance.  It is more difficult than expected to beat the TDI-CCD noise-free charge transfer in sub-pixel steps in combination with a low dark current.  Depending on which side of the table you are sitting, this can be bad news (for the customers eagerly waiting for TDI-CMOS) or this can be good news (for the engineers, because there are still enough challenging developments ahead of us).

Albert, 26-11-2015.

Here are some very first reactions of people who read the PDAF report :

“I went through the report and I find it well written with a lot of information” (R.P.)

“Great systematic analysis” (M.G.)

“We find the content useful for our work” (D.A.)

Albert, 20/11/2015.

 

 

 

and the report is fine.