Analysis and Design of Analog Integrated Circuits Lecture 14. Noise Spectral Analysis for Circuit Elements

Transcription

1 Analysis and Design of Analog Integrated Circuits Lecture 14 Noise Spectral Analysis for Circuit Elements Michael H. Perrott March 18, 01 Copyright 01 by Michael H. Perrott All rights reserved.

2 Recall Frequency Domain View of Random Process It is valid to take the FFT of a sequence from a given trial However, notice that the FFT result changes across trials - Fourier Transform of a random process is undefined! - We need a new tool called spectral analysis noise[n] (Trial 1) noise[n] (Trial ) noise[n] (Trial 3) n n n Magnitude of fft of noise[n] (Trial 1) λ Magnitude of fft of noise[n] (Trial ) λ Magnitude of fft of noise[n] (Trial 3) λ

3 Expectation of a Random Variable The expectation of random variable y is defined as - We see that: E(y) = E(y) = E((y μ y ) )= Z Z Z yf y (y)dy yf y (y)dy = μ y - In the case where y = 0 (i.e., the mean of y is 0) E(y )=E((y μ y ) )=σ y (y μ y ) f y (y)dy = σ y E(y ) is called the second moment of random variable y 3

4 Independence of Random Variables Consider two random variables x and y - x and y are said to be independent if and only if Where f(x,y) is the joint probability distribution of x and y - which implies E(xy) = Z f(x, y) =f(x)f(y) xyf(x, y)dxdy = Z xf(x)dx Z yf(x)dy E(xy) =E(x)E(y) The above relationship is also true under a less strict condition called linear independence If x and y are zero mean, then E(xy) = 0 implies that x and y are uncorrelated 4

5 Autocorrelation and Spectral Density (Discrete-Time) Assume a zero mean, stationary random process x[n]: - The autocorrelation of x[n] is defined as: Note that: R xx [m] =E(x[n] x[n + m]) R xx [0] = E(x [n]) = σ x - The power spectral density of random process x[n] is defined as S x (λ) = X m= R xx [m]e jπλm Note that = ft, where f is frequency (in Hz) and T is the sample period of the process (in units of seconds) Power spectral density of x[n] is essentially the (Discrete- Time) Fourier Transform of the autocorrelation of x[n] 5

6 Implications of Independence (Discrete-Time) If the samples of a zero mean random process, x[n], are independent of each other, this implies R xx [m] =E(x[n]x[n + m]) ½ E(x = [n]) = σx, m =0 E(x[n])E(x[n + m]) = 0, m 6= 0 The corresponding power spectral density is then calculated as X S x (λ) = R xx [m]e jπλm = σx m= - This is a known as a white random process, whose spectral density is flat across all frequencies 6

7 Understanding White Random Processes noise[n] (Trial = 1) Histogram of 100 samples Independence between samples implies that previous samples provide no benefit in trying to predict the value of the current sample For Gaussian white processes, the best we can do is use the Gaussian PDF to determine the probability of a sample being within a given range - Variance of the process is a n Histogram of 1,000 samples Histogram of 10,000 samples Histogram of 1,000,000 samples sample value sample value sample value sample value key parameter 7

8 Spectral Density of a White Process (Discrete-Time) noise[n] (Trial 1) Magnitude of fft of noise[n] (Trial 1) n λ Spectral Density of noise[n] σ x λ The spectral density of a white process is well defined - This is in contrast to the FFT of a white process, which varies between different trials of the process - Note that the spectral density is double-sided since it is based on the Fourier Transform (which is defined for both positive and negative frequencies) 8

9 Autocorrelation and Spectral Density (Continuous-Time) Assume a zero mean, stationary random process x(t): - The autocorrelation of x(t) is defined as: Note that R xx (τ) =E(x(t) x(t + τ)) R xx (0) = E(x (t)) = σ x - The power spectral density of random process x(t) is defined as S x (f) = Z τ= R xx (τ)e jπfτ dτ Again, the power spectral density corresponds to the Fourier Transform of the autocorrelation function of the random process x(t) 9

10 White Random Process (Continuous-Time) Assume a zero mean, stationary random process x(t): - Assuming that the samples of a random process, x(t), are independent of each other, this implies R xx (τ) =E(x(t)x(t + τ)) = N o δ(t) Where (t) is known as the delta function with properties: δ(t) =0 fort 6= 0, - The power spectral density of x(t) is then: S x (f) = Z τ= Z δ(t)dt =1 R xx (τ)e jπfτ dτ = N o As with a discrete-time white process, a continuous-time white process has flat spectral density across all frequencies Note that the variance of a white process is actually infinite Practical white noise is bandlimited and has finite variance 10

11 Spectral Density of a White Process (Continuous-Time) Noise(t) (Trial 1) Spectral Density of Noise(t) 0 t N o 0 f As with a discrete-time, white process, the spectral density of a continuous-time, white process is well defined - It is flat with frequency - For analog circuits, units of N o are V /Hz or A /Hz - It is double-sided, meaning that it is defined for both positive and negative frequencies 11

12 Spectral Density Calculations Involving Filtering S x (f) S y (f) N o x(t) H(s) y(t) 4N o 0 f H(f) -f -f 1 0 f 1 f f -f -f 1 0 f 1 f Assuming an input random process x(t) is fed into a linear, time-invariant filter H(s), the resulting power spectral density of the output random process y(t) is calculated as: S y (f) = H(f) S x (f) - Note that filtering a white random process leads to a new random process that is no longer white The output spectral density is no longer flat across frequency f Different output samples in time are no longer independent 1

13 Spectral Density Calculations Involving Power S x (f) S y (f) N o x(t) H(s) y(t) 4N o 0 f H(f) -f -f 1 0 f 1 f f The power (i.e., variance) of a zero mean random process corresponds to the integration of its power spectral density P y = σ y = R yy (0) = -f -f 1 Z 0 f 1 f S y (f)df = - Note that we can consider the power in certain frequency bands by changing the value of f 1 and f - In the above example: f Z f1 f P y =4N o (f f 1 ) S y (f)+ Z f f 1 S y (f) 13

14 Double-Sided Versus Single-Sided Spectral Densities S x (f) S y (f) N o x(t) H(s) y(t) 8N o 0 f H(f) 0 f 1 f f It turns out that power spectral densities are always symmetric about positive and negative frequencies Single-sided spectral densities offer a short cut in which only the positive frequencies are drawn - In order to conserve power, the spectral density magnitude is doubled - For the above example: 0 f 1 f f P y =8N o (f f 1 ) We will use only single-sided spectral densities in this class 14

15 Noise in Resistors Corresponds to white noise (i.e., thermal noise) in terms of either voltage or current R v n R i n v n =4kTR f i n =4kT 1 R f - Circuit designers like to use the above notation in which v n n and i n i n represent power in a given bandwidth f in units of Volts or Amps, respectively k = J/K - k is Boltzmann s constant: - T is temperature (in Kelvins) Usually assume room temperature of 7 degrees Celsius T =300K 15

16 Noise In Inductors and Capacitors Ideal capacitors and inductors have no noise! C L In practice, however, they will have parasitic resistance - Induces noise - Parameterized by adding resistances in parallel/series with inductor/capacitor Include parasitic resistor noise sources 16

17 Noise in CMOS Transistors (Assumed in Saturation) G I D D Transistor Noise Sources Drain Noise (Thermal and 1/f) S Gate Noise (Induced and Routing Parasitic) Modeling of noise in transistors includes several noise sources - Drain noise Thermal and 1/f influenced by transistor size and bias - Gate noise Induced from channel influenced by transistor size and bias Caused by routing resistance to gate (including resistance of polysilicon gate) Can be made negligible with proper layout such as fingering of devices We will ignore gate noise in this class 17

18 Drain Noise Thermal (Assume Device in Saturation) i nd V GS G V D >ΔV S D Thermally agitated carriers in the channel cause a randomly varying current =4kTγg dso f th i nd - is called excess noise factor = /3 in long channel = to 3 (or higher!) in short channel MOS devices - g dso will be discussed shortly i nd Δf 4kTγg dso (Note: g dso = g m /α) f 18

19 Drain Noise 1/f (Assume Device in Saturation) i nd V GS G V D >ΔV S D Traps at channel/oxide interface randomly capture/release carriers i nd Δf - Parameterized by K f K f provided by fab Sometimes K f of PMOS << K f of NMOS due to buried channel 4kTγg dso - To minimize: want large area (high WL) drain 1/f noise 1/f noise corner frequency drain thermal noise f 19

20 Drain-Source Conductance: g dso g dso is defined as channel resistance with V ds =0 - Transistor in triode, so that dv ds g dso = di d Vds =0 = μ n C ox W L (V gs V T ) - Ideally equals g m, but effects such as velocity saturation can cause g dso to be different than g m 0

21 Plot of g m and g ds versus V gs for 0.18 NMOS Device 4 Transconductances g m and g do versus Gate Voltage V gs I d 3.5 V gs M 1 W L = 1.8μ 0.18μ Transconductance (milliamps/volts) g dso g d0 =μ n C ox W/L(V gs -V T ) g m (simulated in Hspice) Gate Voltage V gs (Volts) For V gs bias voltages around 1. V: α = g m 1 g dso 1

22 Plot of g m and g ds versus I dens for 0.18 NMOS Device Transconductances g m and g do versus Current Density 4 I d 3.5 V gs M 1 W L = 1.8μ 0.18μ Transconductance (milliamps/volts) g dso g d0 =μ n C ox W/L(V gs -V T ) g m (simulated in Hspice) Current Density (microamps/micron)

23 Key Noise Sources for Noise Analysis v ng =4kTR G f v nd R D v nd =4kTR D f R G v ng v gs C gs g m v gs g mb v s r o i nd I R D D R G V out v s v ns v ns =4kTR S f V in R S R S Transistor drain noise: i nd =4kTγg dso f + K f f Thermal noise g m WLC ox 1/f noise f 3

24 Useful References on MOSFET Noise B. Wang et. al., MOSFET Thermal Noise Modeling for Analog Integrated Circuits, JSSC, July 1994 Jung-Suk Goo, High Frequency Noise in CMOS Low Noise Amplifiers, PhD Thesis, Stanford University, August Jung-Suk Goo et. al., The Equivalence of van der Ziel and BSIM4 Models in Modeling the Induced Gate Noise of MOSFETS, IEDM 000, Todd Sepke, Investigation of Noise Sources in Scaled CMOS Field-Effect Transistors, MS Thesis, MIT, June

25 Input Referral of Noise V in K V out V in K V out v ns 1 K v ns It is often convenient to input refer the impact of noise when performing noise analysis in circuits - To justify the above, recall that filtering a random process x(t) leads to an output random process y(t) such that S y (f) = H(f) S x (f) For the case where H(f) = K (i.e., a simple gain factor): S y (f) = K S x (f) S x (f) = 1 K S y(f) 5

26 Example: Common Source Amplifier v nd R D Input-refer the noise sources (apply superposition) R D V out V out i nd V in V in 1 g m i nd 1 ((R D r o )g m ) v nd Note that we will always assume that different circuit elements produce uncorrelated noise Can directly add the voltage noise sources (in power, not voltage) if they are uncorrelated V in 1 g m i nd 1 ((R D r o )g m ) v nd R D V out 6

27 Summary Power spectral density provides a rigorous approach to describing the frequency domain behavior of the ensemble behavior of stationary, ergodic (zero mean) random processes - Key concepts: Expectation, Autocorrelation, Fourier Transform, Correlation, Filtering Circuit designers like the following notation - Single-sided rather than double-sided spectra - Voltage and current noise power denoted as and v n i n Key noise properties of circuit elements - Resistor: thermal noise (white noise) - MOS transistor: thermal + 1/f noise Useful analysis tool: input referral of noise sources - Assumption of uncorrelated noise from different elements allows their power (i.e., variance) to be added 7