from atoms to bits
play

From Atoms to Bits Ahmet Onat 2018 onat@sabanciuniv.edu Layout of - PowerPoint PPT Presentation

From Atoms to Bits Ahmet Onat 2018 onat@sabanciuniv.edu Layout of the Lecture Analog interfacing to sensors: Signal conditioning Sampling and quantization Bridge circuits and instrumentation amplifiers Linearization


  1. From Atoms to Bits Ahmet Onat 2018 onat@sabanciuniv.edu

  2. Layout of the Lecture  Analog interfacing to sensors:  Signal conditioning  Sampling and quantization  Bridge circuits and instrumentation amplifiers  Linearization  Design for low power  Digital interfacing to sensors

  3. Desirable Sensor Characteristics  Sensor reading equal to the measured quantity  Suitable  accuracy, precision,  range, sensitivity → gain  resolution, etc.  Low noise  Linearity

  4. Characteristics of Instrumentation  Accuracy: How close is the measurement to measured.  Precision: What is the uncertainty in the measurement.  Range: Which value interval is measurable?  Sensitivity: For a given change in input, the amount of the change in output.  Resolution: Smallest amount of measurable change  Repeatability: Under the same conditions, can we get the same measurement?

  5. Accuracy - Precision  How accuracy and precision are related? Accurate, (In)accurate, Inaccurate, Precise Imprecise Precise  Inaccurate but precise?  Metal ruler on a hot day: Same precision bad accuracy

  6. Sensitivity - Range  Generally high sensitivity sounds good.  However, high sensitivity restricts range.  Deliberately→nonlinear sensor can be used. High sensitivity Low sensitivity Nonlinear  1mV precision;  8bit: 0.256 V range  12bit: 4.096 V

  7. Analog Interfacing to Sensors There are 3 main stages in sensing:  Physics  Electronics  Information  →Pysics will not be treated.

  8. Signal Conditioning Electronics

  9. Signal Conditioning System 1.Sensor Output 2.Preamplifier stage 3.Removal of offset 4.Antialiasing filter 5.Amplifier

  10. Signal Conditioning: Sensor 1.Sensor Low voltage  Low power electrical signal → Low current  Wide frequency bandwidth  Aliasing during sampling  Offset voltage  Prevents use of full quantizer range

  11. Signal Conditioning: Sensor 1.Sensor  Voltage source with impedance  OR P o max → r o = r i r i →∞ : V s = X s  r i = V s / i i (Calculate like a voltage divider)

  12. Signal Conditioning: Preamplifjer 2. Preamplifier stage  Extract largest amount of power from signal or,  Draw the least amount of current.  Matched impedance circuit  Low noise  High gain

  13. Signal Conditioning: Preamplifjer  Draw the least amount of current: Voltage follower configuration 17 Ω r i = 2 × 10  Susceptibility to ESD increases.

  14. Signal Conditioning: Ofgset Removal Xp 3. Offset remove  The information content is confined to Information content a small part of the signal range. No information  Amplification will not allow t max precision of the quantizer: Xo 2 MSB always set: 11xxxxxx 12 bit ADC → 10bit ADC t

  15. Signal Conditioning: Ofgset Removal 3. Offset remove  Difference amplifier. V o = R f ( V p − V off ) R 1 : Constant offset voltage for removal.  V o f f

  16. Signal Conditioning: Filter 4. Antialiasing Filter  “A bandlimited function is completely determined by its samples taken at more than twice the maximum frequency component”  It is necessary to limit the bandwidth of the signal for:  Sampling  Noise suppression

  17. Signal Conditioning: Filter  Filter characteristic:  Passband ripple must be less than ADC resolution. Passband  Bandwidth limit frequency 2 − N at 2 -N gain. Stopband  What order filter? f max

  18. Signal Conditioning: Amplifjer Xa 5. Amplification  Signal is amplified to the reference voltage of the ADC. x a ( t )< x max = V ref t

  19. Signal Conditioning: Amplifjer  Simple non-inverting amplifier circuit. V o =( 1 + R f  Ideal gain ( A ≈∞): ) V i R 1 = A ( R 1 + R f ) V o  Actual gain: V i AR 1 + R 1 + R f  Error for A=50,000, R 1 =1kΩ, R 2 =9kΩ , V i =0.500V : V o ∞ = 5.000 V e = 2000 μ V V o 50 k = 4.998 V 2 counts on the quantizer Δ = 1221 μ V  For 5V , 12bit:

  20. Data Converter 6.Sample and Hold 7.Quantizer

  21. Sample and Hold  Ideal sampling requires  zero duration and  infinite currents. Ideal sample and hold  Actual sampling uses a transistor… Actual sample and hold  The body resistance of the transistor turns the S&H into a low pass filter. Sample and hold equivalent circuit

  22. Sample and Hold  Time constant of a 1 st order RC filter: τ= RC s   It is necessary to keep sampling for 5 τ at least to allow the capacitor to be charged to V a  Microcontrollers allow the adjustment of the charging period.  Higher precision ADC requires longer charge times: “Acquisition Time” f = 1 5 τ  It is not possible to exceed for sampling.

  23. Sample and Hold  Sampling several signals at the same instant.  Several ADC can be used.  More commonly, synchronous sampling, sequential conversion:  In specialized applications several ADC are used: Motor current sampling, lab measurement etc.

  24. Sampling of Continuous Time Signals  The Fourier transform of a continuous time signal is given by: ∞ X ( f )= ∫ − 2 π f t dt x ( t ) e −∞ x s ( t ) : x ( nT s ) ; T s = 1 / f s ∞ X s ( f )= ∑ X ( f + kf s ) k =−∞  When a signal is sampled by f s , its frequency spectrum becomes periodic by f s .

  25. Sampling of Continuous Time Signals Sampled. Note spacing X ( ƒ) ƒ −B B Continuous time signal frequency spectrum With correct filtering, original signal can be exactly recovered. Source of figures: Wikipedia.org

  26. Sampling of Continuous Time Signals  However, if low sampling frequency is used: ∞ X s ( f )= ∑ X ( f + kf s ) k =−∞  There are overlaps: [( k + 1 ) f s − B,kf s + B ] ,k ∈−∞ , ∞ Which are added up.  Original signal is lost. Source of figures: Wikipedia.org

  27. The Data Converter AKA Quantizer  Analog to digital conversion (ADC) is a search operation. x q = ⌊ 2 2 ⌋ N V in + Δ V ref N Δ = V ref / 2  Precision is limited to finite value,  Information about input is lost.  Time consuming OR complex operation.

  28. The Data Converter AKA Quantizer  Ideal, normalized, 3 bit quantizer. Source of figures: D.H. Sheingold, Analog Digital Conversion Handbook, 1986

  29. Quantization Error as Linear Noise  V in is ambiguous→  Quantization can be modeled as additive noise. x q = V in + n q

  30. Quantization Error as Linear Noise  Vin is not known→  Quantization can be modeled as additive noise. x q = V in + n q SNR dB = 6.02 N + 1.76 ( V in = Asin (ω t ) , N bit quantizer )

  31. Quantizer Performance  Gain not unity:  Does not start from zero:  Step change voltages are not uniform:  Each can be corrected in software (not easily) Source of figures: D.H. Sheingold, Analog Digital Conversion Handbook, 1986

  32. Quantizer Realizations: Flash  Low latency  High complexity O(2^N)  Bad linearity

  33. Quantizer Realizations: Successive Approx.  Higher latency.  Low complexity.  Good linearity. Source of figures: D.H. Sheingold, Analog Digital Conversion Handbook, 1986

  34. Digital Signal Processing

  35. From Physical Quantity to Physical Value  The final stage is digital signal processing.

  36. Oversampling / Noise Shaping  Signal is sampled at much higher rate than Shannon.  After ADC, DSP low pass filter is applied.  Low order anti-aliasing filter is sufficient.  Increase in precision is obtained due to averaging. LPF V a X q + S&H ↓ OSR ω c ω c =π/ OSR f s = 2 f m × OSR n q Electronics Information

  37. Oversampling / Noise Shaping  Sampling rate is much higher than required by Shannon theorem.  Quantization noise power is constant, regardless of sampling rate.  Signal spectrum amplitude is inreased proportionally. V a ( f ) ' = V a ( f )× OSR  Signal occupies less of the digital bandwith. f ' max = f max / OSR

  38. Oversampling / Noise Shaping  Downsampling by OSR brings the signal back to desired band. X q ↓ OSR

  39. Oversampling / Noise Shaping  Oversampling increases the ADC precision. 4 w  OSR= → w bit increase in quantizer precision.  For 4 bit increase: OSR= =256 times oversampling. 4 4  44.1KSPS → 11.3MSPS is too much!  Oversampling can be augmented with noise shaping to improve ratio.

  40. Oversampling with Noise Shaping  Quantization noise is injected during ADC.  The fedback system causes the quantization noise spectrum to be  low at low frequencies.  Higher at high frequencies. LPF Sampled V a X q + S/H ADC ↓ OSR data ω c Integrator π/ OSR f s = 2 f m × OSR DAC Information Electronics

  41. Oversampling with Noise Shaping  The feedback loop has different gains for  quantization noise and  Signal.  Quantization noise is concentrated towards higer frequencies.  OSD=8 is sufficient for 4 bit increase vs. OSD=256

  42. High Precision Applications

  43. Reference Voltage  Changes in V ref have the same effect as changing the input voltage.  Compensation for:  Temperature  Manufacturing tolerances x q = ⌊ 2 2 ⌋ , Δ = V ref / 2 N V in + Δ N V ref

  44. Reference Voltage Tolerance  LM336A-2.5: 2.5 V reference diode.  2.44 ~ 2.54 V at 25 o . V ref = 2.44 V → x q = 100  8 bit ADC, V in =1V : V ref = 2.54 V → x q = 104  How to calibrate?

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend


More recommend