1. Ph. Farthouat CERN ELEC 2002 ADC 1 Analog to Digital Conversion  Introduction  Main characteristics –Resolution –Dynamic range –Bandwidth –Conversion time  Linearity –Integral –Differential  Different types –Successive approximation –Slope integration –Flash  FADC  Sigma Delta  Applications

2. Ph. Farthouat CERN ELEC 2002 ADC 2 Analog to Digital Converter  Analog input - Digital output –Most of the time commercial ASICs –Converts voltage or current  What is to be converted? –Voltage, Current, Charge, Time –Analog input processing is necessary »To convert the measured quantity in a tension »To adapt the impedances »To filter »To adapt the amplitude  What is the expected resolution?  What is the dynamic range?  What is the expected linearity?  How often is a conversion needed?

3. Ph. Farthouat CERN ELEC 2002 ADC 3 Resolution  An ADC is given as an n-bit ADC  The least significant bit gives the resolution of the ADC  Related to full scale if the ADC is linear –LSB = A/2 n –Linear 8-bit ADC with a 1V full scale input –Resolution = 1/2 8 = 3.9 mV (0.39%)

4. Ph. Farthouat CERN ELEC 2002 ADC 4 Dynamic range  Ratio between the minimum and the maximum amplitude to be measured –e.g. calorimeter signal 10 MeV to 2 TeV gives a 2 10 6 dynamic range  In case of linear system the dynamic range is related to the number of bits (and hence the resolution) –an 8-bit ADC has a 256 dynamic range  In case of large dynamic range (as for a calorimeter) some non- linearity has to be introduced –linear ADC for the previous example would require 21 bits!  Often used terms in physics: –n-bit resolution –n-bit dynamic range –example: »8-bit resolution for a 12-bit dynamic range means that a signal in the range 1- 4000 is measured with a resolution of 0.39%

5. Ph. Farthouat CERN ELEC 2002 ADC 5 Conversion time and Bandwidth  How often can a conversion be done –a few ns to a few ms depending on the technology »100 MHz FADC to slow sigma-delta  Input bandwidth –Maximum input signal bandwidth »Track and hold input circuitry »Conversion frequency (FADC)

6. Ph. Farthouat CERN ELEC 2002 ADC 6 ADC transfer curve  Ideal ADC  Errors –Offset –Integral non-linearity –Differential non-linearity

7. Ph. Farthouat CERN ELEC 2002 ADC 7 Integral linearity  Non linearity: maximum difference between the best linear fit and the ideal curve Non Linearity

8. Ph. Farthouat CERN ELEC 2002 ADC 8 Differential non-linearity  Least Significant Bit (LSB) value should be constant but it is not  The difference with the ideal value shall not exceed 0.5 LSB  Easy way of seeing the effect –random input covering the full range –frequency histogram should be flat –differential non-linearity introduces structures

9. Ph. Farthouat CERN ELEC 2002 ADC 9 Types of ADC  Successive approximation  Single slope integration  Dual slope integration  Flash ADC  Sigma-Delta

10. Ph. Farthouat CERN ELEC 2002 ADC 10 Successive approximation  Compare the signal with an n-bit DAC output  Change the code until –DAC output = ADC input  An n-bit conversion requires n steps  Requires a Start and an End signals  Typical conversion time –1 to 50  s  Typical resolution –8 to 12 bits  Cost –15 to 600 CHF

11. Ph. Farthouat CERN ELEC 2002 ADC 11 Single slope integration  Start to charge a capacitor at constant current  Count clock ticks during this time  Stop when the capacitor voltage reaches the input  Cannot reach high resolution –capacitor –comparator - + IN C R S Enable N-bit Output Q Oscillator Clk Counter Start Conversion Vin Counting time

12. Ph. Farthouat CERN ELEC 2002 ADC 12 Dual slope integration (Wilkinson)  Capacitor charged with a current proportional to the input during a fixed time  Discharge at constant current  Count of clock ticks during the discharge Counting time Charge with a current proportional to the input

13. Ph. Farthouat CERN ELEC 2002 ADC 13 Dual slope integration (2)  Advantages –Capacitor value is not important although has to be of good quality –Comparator error can be canceled by beginning and ending each conversion cycle at the same voltage –Clock frequency errors can be cancelled by using the same clock to define the charge time  Typical resolution –10 to 18 bit  Conversion time –Depends on the clock frequency

14. Ph. Farthouat CERN ELEC 2002 ADC 14 Flash ADC  Direct measurement with 2 n -1 comparators  Typical performance: –4 to 10-12 bits –15 to 300 MHz –High power  Half-Flash ADC –2-step technique »1st flash conversion with 1/2 the precision »Subtracted with a DAC »New flash conversion  Waveform digitizing applications

15. Ph. Farthouat CERN ELEC 2002 ADC 15 Flash ADC (cont)  Pipeline ADC  Input-to-output delay = n clocks for n stages  One output every clock cycle  Saves power (less comparators) S&H 3-bit FADC3-bit DAC - X 4 3-bit S&HStage 1Stage 2Stage 3Stage 44-bit FADC Time Adjustment & Digital Error Correction 3-bit 4-bit 12-bit Input

16. Ph. Farthouat CERN ELEC 2002 ADC 16 Effective number of bits  Effective number of bits of an n-bit FADC –n’ giving the correct SNR  Example: AD9235 12-bit 20 to 65 MHz –SNR = 70 dB –Effective number of bits = 11.4  (x) q  An n bit ADC introduces a quantization error  Encoding a signal (A/2) sin  t with A being the full scale will give an error  Signal to Noise Ratio

17. Ph. Farthouat CERN ELEC 2002 ADC 17 Shannon Theorem  A signal x(t) has a spectral representation |X(f)|; X(f) = Fourier transform of x(t)  A signal x(t) after having been digitised at the frequency f s, has a spectral representation equal to the spectral representation of x(t) shifted every f s  If X(f) is not equal to zero when f > f s /2, there is spectrum overlapping  The Shannon theorem says that x(t) can be reconstructed after digitisation if the digitising frequency is at least twice the maximum frequency in x(t) spectral representation  This is mathematical only, as it supposes perfect filtering

18. Ph. Farthouat CERN ELEC 2002 ADC 18 Example (1)  “Typical” physics pulse –100 ns rising and falling edge  Effect of a digitisation at 10 MHz and 20 MHz

19. Ph. Farthouat CERN ELEC 2002 ADC 19 Example (2)  100 ns square pulse  Digitisation at 10 MHz and 20 MHz

20. Ph. Farthouat CERN ELEC 2002 ADC 20 Using FADC  Do not forget to make a frequency analysis of the signal –Any spectrum overlapping introduces noise –Take into account the effective number of bits  Filtering is necessary –Before digitisation (analog) to cut the input signal frequency spectrum –After digitisation (digital) to extract the signal frequency spectrum and to compensate the effect of digitisation over a finite time window -T0 +T0 1/2*T0

21. Ph. Farthouat CERN ELEC 2002 ADC 21 Over-sampling ADC  If f s is higher than the frequency f 0 of the signal to be measured then after filtering the error will become  (x) q  Assuming the error is a white noise, its power spectral density is flat within the range [–f s /2,f s /2] -fs/2+fs/2 f |  (f)|

22. Ph. Farthouat CERN ELEC 2002 ADC 22 Over-sampling ADC (cont)  Hence it is possible to increase the resolution by increasing the sampling frequency and filtering  Example : an 8-bit ADC becomes a 9-bit ADC with an over-sampling factor of 4 –But the 8-bit ADC must meet the linearity requirement of a 9-bit  The signal to noise ratio when encoding a signal (A/2) sin  t, with A being the full scale, will be

23. Ph. Farthouat CERN ELEC 2002 ADC 23 Sigma-Delta ADC  The output of this modulator is a digital stream –Average = Input  Over-sampling ratio M=f s /f 0 1-bit ADC 1-bit DAC - Input Output 1rst Order Sigma-Delta Modulator

24. Ph. Farthouat CERN ELEC 2002 ADC 24 Sigma-Delta ADC (cont)  Gain of 1.5 bits per octave increase of M –M = 2350 to have a 16-bit ADC  Higher orders sigma-delta are implemented  Examples (Analog Devices) –16-bit, 2.5 MHz –24-bit, 1kHz The design of low-voltage, low-power sigma-delta modulators Shahriar Rabii & Bruce Wooley Kluwer academic publisher  The signal to noise ratio when encoding a signal (A/2) sin  t, with A being the full scale, will be

25. Ph. Farthouat CERN ELEC 2002 ADC 25 Resolution/Throughput Rate

26. Ph. Farthouat CERN ELEC 2002 ADC 26 Power  Power is going down  Examples –8-bit, 200MSPS: 1.3 mW/MSPS –10-bit, 10 MSPS core used in ALICE TPC read-out: <20 mW –24-bit, 1 kSPS: 45 mW

27. Ph. Farthouat CERN ELEC 2002 ADC 27 Applications  In HEP we are facing large number of channels  The quantity to be measured depends on the type of detector –Charge in the case of a lead glass calorimeter with PM read-out –Voltage in the case of a lead glass calorimeter with triode and preamplifier shaper read-out  Low cost Charge integrating ADC for a LEP calorimeter  High speed peak sensing ADC for a neutrino experiment  Non linear ADC for an LHC experiment  FADC with numerical filtering for an LHC trigger application

28. Ph. Farthouat CERN ELEC 2002 ADC 28 Charge integrating ADC (1)  High resolution: 12-bit  High dynamic range: 15-bit  High density: 96 channel per Fastbus board  Low speed: 1 ms conversion time  Low cost per channel  Principle: –Single ADC for 48 channels –Charge input integration and storage

29. Ph. Farthouat CERN ELEC 2002 ADC 29 Charge integrating ADC (2)  Block diagram

30. Ph. Farthouat CERN ELEC 2002 ADC 30 Charge integrating ADC (3)  Performance –12-bit resolution, 15-bit dynamic range –Conversion time t cvt = 48 (t c + t s ) = 960 µs »where t c = ADC conversion time = 12 µs »and t s = settling time for multiplexer and amplifiers = 8 µs.

31. Ph. Farthouat CERN ELEC 2002 ADC 31 Peak sensing ADC (1)  12-bit resolution  Low dead-time : 8  s  Data buffering

32. Ph. Farthouat CERN ELEC 2002 ADC 32 Peak sensing ADC (2)  Block diagram Vin - + C - + ADC FIFO Read-out 12-bit Gate

33. Ph. Farthouat CERN ELEC 2002 ADC 33 ADC for an LHC experiment (1)  ATLAS Liquid Argon calorimeter  High dynamic range: 16-bit  Shaping of the signal to minimise pile-up  Sampling every 25 ns (bunch crossing period)  Level-1 pipeline Shaping

34. Ph. Farthouat CERN ELEC 2002 ADC 34 ADC for an LHC experiment (2)  Block diagram

35. Ph. Farthouat CERN ELEC 2002 ADC 35 ADC for an LHC experiment (3)  Performance –Pedestal stability to 0.1 ADC counts –Noise equivalent to 20 MeV –Integral non-linearity below 0.25% –Conversion time : 25 ns per sample

36. Ph. Farthouat CERN ELEC 2002 ADC 36 FADC for LHC trigger purpose (1)  Analog summation on the detector to form the trigger tower  Shaping time covers several bunch crossings  FADC and numerical filtering to: –Extract the energy –Extract the bunch crossing responsible for it

37. Ph. Farthouat CERN ELEC 2002 ADC 37 FADC for LHC trigger purpose (2)  Block diagram

38. Ph. Farthouat CERN ELEC 2002 ADC 38 FADC for LHC trigger purpose (3)  Filter algorithm : Finite Impulse Response