Mixed Signal IC Design Notes set 6: Mathematics of Electrical Noise

Transcription

1 ECE45C /8C notes, M. odwell, copyrighted 007 Mied Signal IC Design Notes set 6: Mathematics o Electrical Noise Mark odwell University o Caliornia, Santa Barbara rodwell@ece.ucsb.edu , a

2 Strategy ECE45C /8C notes, M. odwell, copyrighted 007 There is not time in45c/8c to develop this subject in detail. Strategy : give backround suicient or correct calculation o SN, spectral densities, correlation unctions, signal correlations, error rates. More detail can be ound in my noise class notes (on the web), or in the literature. an der Zeil's book iscomprehensive.

3 Topics ECE45C /8C notes, M. odwell, copyrighted 007 Math: distributions, random variables, epectations, pairs o, joint distributions, covariance and correlations. andom processes, stationarity, ergodicity, correlation unctions, autocorrelation unction, power spectral density. Noise models o devices: thermal and shot noise. Models o resistors, diodes, transitors, antennas. Circuit noise analysis: network representation. Solution. Total output noise. Total input noise. generator model. En/In model. Noise igure, noise temperature. Signal / noise ratio.

4 random variables ECE45C /8C notes, M. odwell, copyrighted 007

5 The irst step: random ariables ECE45C /8C notes, M. odwell, copyrighted 007 During an eperiment, a random variable The probability that lies between P{ < < } ( ) d and ( ) is the probability distribution unction. () takes on a particular is value.

6 Eample: The Gaussian Distribution ECE45C /8C notes, M. odwell, copyrighted 007 The Gaussian distribution : ( ) πσ ep ( ) σ We will deine shortly the mean ( ) and the standard deviation ( σ ). Because o the * central limit theorem*, physical random processes arising rom the sum o many small eects have probability distributions close to that o the Gaussian. () ~σ

7 Mean values and epectations ECE45C /8C notes, M. odwell, copyrighted 007 Epectation o a unction g() E [ g( ) ] + g( ) ( ) d o the random variable Mean alue o E [ ] + ( ) d Epected value o E + [ ] ( ) d

8 ariance ECE45C /8C notes, M. odwell, copyrighted 007 The variance σ o rom itsaverage value σ is its root ( ) E ( ) [ ] ( ) - mean - square deviation + ( ) d The standard deviation σ the square root o the variance o is simply

9 eturning to the Gaussian Distribution ECE45C /8C notes, M. odwell, copyrighted 007 The notation describing the Gaussian distribution : ( ) should now be clear. ( ) ep πσ σ () ~σ

10 ECE45C /8C notes, M. odwell, copyrighted 007 ariance vs Epectation o the Square ( ) ( )( ) ( ) ( ) ( ) ( ) the epectation. minus the square o the square the epectation o variance is The σ σ

11 Pairs o andom ariables ECE45C /8C notes, M. odwell, copyrighted 007 To understand random processes, we must irst understand pairs o random variables. In an eperiment, a pair o random variables takes on speciic particular values and y. and Y Their joint behavior is described by the joint distribution Y (, y) P{ A < < B and C < y < D} D C B A Y (, y) ddy

12 Pairs o andom ariables ECE45C /8C notes, M. odwell, copyrighted 007 Marginal distributions must also be deined P{ A < < B } + B A B A Y (, ( ) d y) ddy and similarly or Y : P{ C < y < D } D + C D C Y Y (, ( y) dy y) ddy

13 Statistical Independence ECE45C /8C notes, M. odwell, copyrighted 007 In the case where Y (, y) ( ) Y ( y), the variables are said to be statistically independent. Thisisnot generally epected.

14 Epectations o a pair o random variables ECE45C /8C notes, M. odwell, copyrighted 007 The epectation o a unction g(, Y) E [ g(, y) ] + + g(, y) Y (, y) ddy o the random variables Y and Y is Epectation o : E [ ] + + Y (, y) ddy + ( ) d Epectation o E [ ] + + Y (, y) ddy + ( ) d...and similarly or Y and Y.

15 Correlation between random variables ECE45C /8C notes, M. odwell, copyrighted 007 The correlation o and Y is Y [ Y ] + + E y Y (, y) ddy The covariance o and Y is C Y E [( )( Y y) ] E[ Y Y y + y] Y y Note that correlation and covariance are or Y have zero mean values. the same i either

16 Correlation versus Covariance ECE45C /8C notes, M. odwell, copyrighted 007 When we are working with voltages and currents, we usually separate the mean value (DC bias) rom the time - varying component. The random variables then have zero mean. Correlation is then equal to covariance. It is thereore common in circuit noise analysis touse the two termsinterchangably. But, nonzero mean values can return conditional distributions. when we e.g. calculate Be careul.

17 Correlation Coeicient ECE45C /8C notes, M. odwell, copyrighted 007 The correlation coeicient o ρ Y CY / σ σy and Y is Note the (standard) conusion in terminology between correlation and covariance.

18 Sum o TWO andom ariables ECE45C /8C notes, M. odwell, copyrighted 007 Sum o two random variables : Z + Y E I E [ ] [ ] [ Z E ( + Y ) E + Y + Y ] [ ] [ E + E Y ] + and Y both have zero means [ ] [ ] [ Z E + E Y ] + C Y Y This emphasizes the role o correlation.

19 ECE45C /8C notes, M. odwell, copyrighted 007 Pairs o Jointly Gaussian andom ariables [ ] [ ] j i i i i n Y Y Y Y Y Y E E y y y y y and covariances, variances, means which isspeciied by a set o ),,, ( we can have a Jointly Gaussian random vector In general, variables. to a larger # o This deinition can be etended ) ( ) )( ( ) ( ) ( ep ), ( and Y are Jointly Gaussian : I + + σ σ σ σ ρ ρ σ πσ

20 Linear Operations on JG's ECE45C /8C notes, M. odwell, copyrighted 007 I and Y are Jointly Gaussian, and i we deine a + by and W c + dy Then and W are also Jointly Gaussian. This is stated without proo; the result arises convolution o Gaussian unctions produces a Gaussian unction. because The result holds or JGs o any number.

21 ECE45C /8C notes, M. odwell, copyrighted 007 Probability distribution ater a Linear Operation on JG's E σ σ C W W [ ] E[ a + by ] E E W [ ] [ ] [ a E + b E Y ] + ab E[ Y ] [ ] [ ] + [ W c E d E Y ] + cd E[ Y ] [ ] W E[ ( a + by )( c + dy )] E W E ac a + by and W [ + ( ad + bc) Y + bdy ] W [ ] + + [ ] + [ ace ( ad bc) E Y bd E Y ] c W + ( a dy ( a ( c + by ) + by )( c + dy ) + dy ) tedious details We can now calculate the joint distribution o and W. W ( v, w) πσ σ W ρ W ep ( ρ W ) ( v v) σ + ( v v)( w σ σ W w) + ( w w) σ W

22 Why are JG's Important? ECE45C /8C notes, M. odwell, copyrighted 007 The math on the last slide was tedious but there is a clear conclusion : With JG' s subjected o to linear operations, means, correlations, and variances. it is suicient to keep track With this inormation, distribution unctions can always be simply ound. This vastly simpliiescalculations o (linear circuits). noise propagation inlinear systems

23 Uncorrelated ariables. ECE45C /8C notes, M. odwell, copyrighted 007 Uncorrelated : C Y 0 Statistically independent Y (, y) ( ) Y ( y) : Independence implieszero correlation. Zero correlation does not imply independence. For JG' s, uncorrelated does imply independence

24 ECE45C /8C notes, M. odwell, copyrighted 007 Summing o Noise (andom) oltages [ ] the random noise voltages do add. time values o The instantaneous - a correllation term must be included. the two random generators do not add - The noise powers o ) ( The power dissipated in the resistor isa random variable P Two voltages are applied to the resistor C P P E σ σ ρ σ

25 Shot Noise as a andom ariable ECE45C /8C notes, M. odwell, copyrighted 007 The iber has transmission probability p. Send one photon, and call the # o received photons N. E [ ] [ ] and so [ N N p E N p σ E N ] N N p p I we now send many photons ( M o them), transmission o each is statistically independent, so calling the # o received photons N, E [ N ] M E[ N ] Mp and σ N M σ N M ( p p ) Now suppose M >>, p <<, and Mp σ N N The variance o the count approaches >>, the mean value o the count.

26 Thermal Noise as a andom ariable ECE45C /8C notes, M. odwell, copyrighted 007 A capacitor C isconnected to a resistor. The resistor isin equilibrium with a " reservoir" (a warm room) at temperature T can echange energy with the room in the orm o heat. C can dissipate no power : it establishes thermal equilibrium with the room via the resistor. From thermodynamics, any independent degree o at temperature T has mean energy kt/, hence E kt / reedom o a system C / kt / kt / C The noise voltage has variance kt/c.

27 random processes ECE45C /8C notes, M. odwell, copyrighted 007

28 andom Processes ECE45C /8C notes, M. odwell, copyrighted 007 Draw a set o graphs, on separate sheets o unctions o voltage vs. time. o paper, Put them into a garbage can. This garbage can iscalled the probability sample space. Pick out one sheet at random. Thisisour random unction o time. The random process is ( t). The particular outcome is v(t)

29 Time Averages vs. Sample Space Averages ECE45C /8C notes, M. odwell, copyrighted 007 ecall the deinion o the epectation o a unction g() o a random variable E E [ g( ) ] [ g( v( t ))] + g is the *average value* o g, where the average is over the sample space. With our random process deinition, we can deine an average over the [ ( v( t)) ] A g g( ) + + g( v( t g( v( t)) dt ( ) d g sample space at some particular ( v( t time t )) d( v( t We can also deine an average o the unction over time : )) )) :

30 Ergodic andom Processes ECE45C /8C notes, M. odwell, copyrighted 007 An Ergodic random process has averages over time equal to averages over the statistical sample space [ g( v( t ))] A[ g( v( ))] E t In some sense, we have made " random variation with time" equivalent to " random variation over the sample space"

31 Time Samples o andom Processes ECE45C /8C notes, M. odwell, copyrighted 007 With time samples at times t the random process ( t) has and t values ( t ) and ( t ). ( t) and ( t) have some joint probability distribution. They might (or might not) be jointly Gaussian. v(t) v(t ) v(t ) t t t

32 andom Waveorms are andom ectors ECE45C /8C notes, M. odwell, copyrighted 007 Using Nyquist's sampling theorem, i a random signal is bandlimited, and i we pick regularly - spaced time samples t... t n we convert our random process intoa random vector., We can thus analyze random signals using vector analysis and geometry. This is mostly beyond the scope o this class. v(t) t t t n t

33 Stationary andom Processes ECE45C /8C notes, M. odwell, copyrighted 007 The statistics o a stationary process do not vary with time. N E th order stationarity : [ ( ( t ), ( t ),..., ( t ))] E[ ( ( t + τ ), ( t + τ ),..., ( t + τ ))]..and lower orders n n E nd order stationarity : [ ( ( t), ( t) )] E[ ( ( t + τ ), ( t + τ ))] lower orders E[ ( ( t ))] E[ ( ( t + τ ))] v(t) v(t ) v(t ) t t t

34 ECE45C /8C notes, M. odwell, copyrighted 007 estrictions on the random processes we consider We will make ollowing restrictions to make analysis tractable: The process will be Ergodic. The process will be stationary to any order: all statistical properties are independent o time. Many common processes are not stationary, including integrated white noise and / noise. The process will be Jointly Gaussian. This means that i the values o a random process (t) are sampled at times t, t, etc, to orm random variables (t ), etc, then,, etc. are a jointly Gaussian random variable. In nature, many random processes result rom the sum o a vast number o small underlying random processses. From the central limit theorem, such processes can requently be epected to be Jointly Gaussian.

35 ariation o a random process with time ECE45C /8C notes, M. odwell, copyrighted 007 For the random process (t), look at (t ) and (t ). + + E (, ) d [ ] d To compute this we need to know the joint probability distribution. We have assumed a Gaussian process. The above is called the Autocorrellation unction. IF the process is stationary, it is a unction only o (t -t )tau, and hence [ ( )] ( τ) E ( t) t + τ this is the autocorrellation unction. It describes how rapidly a random voltage varies with time. PLEASE recall we are assuming zero-mean random processes (DC bias subtracted). Thus the autocorrellation and the auto-covariance are the same

36 ariation o a random process with time ECE45C /8C notes, M. odwell, copyrighted 007 Note that ( 0) E[ ( t)( t) ] σ gives the variance o the random process. The autocorrelation unction gives us variance o the random process and the correlation between its values or two moments in time. I the process is Gaussian, this is enough to completely describe the process. (τ ) Narrow autocorrelation: Fast variation Broad autocorrelation Slow variation (τ ) τ τ

37 ECE45C /8C notes, M. odwell, copyrighted 007 Autocorrelation is an Estimate o the ariation with Time I random variables and Y are Jointly Gaussian, and have zero mean, then knowledge o the value y o the outome o Y results in a best estimate o as ollows: E[ Y y] Y y Y σ Y y "The epected value o the random variable, given that the random variable Y has value y is..." Hence, the autocorrellation unction tells us the degree to which the signal at time t is related to the signal at time t + τ A narrow autocorrelation is indicative o a quickly-varying random process

38 Power spectral densities ECE45C /8C notes, M. odwell, copyrighted 007 The autocorrellation unction describes how a random process evolves with time. Find its Fourier transorm: + S ( ω) ( τ ) ep( jωτ ) dτ This is called the power spectral density o the signal. emembering the usual Fourier transorm relationships, i the power spectrum is broad, the autocorrellation unction is narrow, and the signal varies rapidly--it has content at high requencies, and the voltages o any two points are strongly related only i the two points are close together in time. I the power spectrum is narrow, the autocorrellation unction is broad, and the signal varies slowly--it has content only at low requencies and the voltages o any two points are strongly related unless i the two points are broadly separated in time.

39 Power spectral densities ECE45C /8C notes, M. odwell, copyrighted 007 S(omega) (tau) (t) omega tau time S(omega) (tau) (t) omega tau time

40 Power Spectral Densities ECE45C /8C notes, M. odwell, copyrighted 007 ecall the Fourier transorm o S ( ω) ( τ ) The inverse transorm holds, so that ( τ ) S ( ω) speciically, (0) So, i σ that the power spectral + π σ + π is called the power spectral ep( + S jωτ ) dτ ep( jωτ ) dω ( ω) the power in the process, density will density is the autocorrelation unction dω give us the power. then integrating This is the justiication or the term," power spectral density"

41 Correlated andom Processes ECE45C /8C notes, M. odwell, copyrighted 007 Two processes can be statistically related. Consider two random processes (t) and Y(t). Deine the cross - correllation unction o Y ( τ ) E[ ( t) Y ( t + τ )] the processes They will have a cross - spectral density as ollows: S Y + ( ω) ( τ ) and thereore Y Y ep( π jωτ ) dτ + ( τ ) S ( ω) ep( jωτ ) dω Y

42 Single-Sided Hz-based Spectral Densities ECE45C /8C notes, M. odwell, copyrighted 007 Double - Sided Spectral Densities S ( τ ) E[ ( t) ( t + τ )] S ( jω) + ( jω) ( τ ) ep( π + jωτ ) dτ ep( jωτ ) dω Single -Sided Hz - based Spectral Densities ~ S ( τ ) E[ ( t) ( t + τ )] S ( j ) + ( j ) ( τ ) ep( jπτ ) dτ + ~ ep( jπτ ) d

43 ECE45C /8C notes, M. odwell, copyrighted 007 Single-Sided Hz-based Spectral Densities- Why? Why this notation? The signal Power ~ S per Hz ( j ) o power in the bandwidth high signal {, } ( j ) d + S ( j ) d S ( j ) is directly the Watts o bandwidth signal at requencies lying close to the requency low low high high low ~ ~ S high low power. ~ d

44 ECE45C /8C notes, M. odwell, copyrighted 007 Single-Sided Hz-based Cross Spectral Densities Double - Sided Cross Spectral Densities S ~ S Y Y Y Y ( τ ) E[ ( t) Y ( t + τ )] S ( jω) + ( jω) ( τ ) Y ( τ ) E[ ( t) Y ( t + τ )] S ( j ) ( j ) ( τ ) Y ep( ep( π jωτ ) dτ Single -Sided Hz - based Cross Spectral Densities ~ Y Y jπτ ) dτ ep( jωτ ) dω ep( jπτ ) d ~ S Y d d ( j ) isalso oten written as Y

45 Eample: Cross Spectral Densities ECE45C /8C notes, M. odwell, copyrighted 007 ( t) ( t) + Y ( t) ( τ ) E[ ( ( t) + Y ( t) )( ( t + τ ) + Y ( t + τ ))] ( τ ) + ( τ ) + ( τ ) + ( τ ) YY Y Y Y P / S * ( jω) S ( jω) + SYY ( jω) + SY ( jω) + SY ( jω) S ( jω) + S ( jω) + e{ S ( jω) } YY Y Or, in single - sided spectral ~ ~ ~ S { } ( j ) S ( j ) + S ( j ) + e S ( j ) YY densities ~ Y

46 Eample: Cross Spectral Densities ECE45C /8C notes, M. odwell, copyrighted 007 The Power [ ( t) ( t) / ] ( 0) E P ( t) / / has epected value Y P / And in the bandwidth between P high low ~ S ( j ) d... low and Integrating with respect to requency (over whatever bandwidth isrelevant) gives the total (epected) power dissipated in. high, Note that the cross - spectral density is relevant.

47 ECE45C /8C notes, M. odwell, copyrighted 007 Noise passing through ilters & linear electrical networks I the ilter has impulse response h( t) and transer unction H ( jω), then or any v in ( t) v out ( t), out ( jω) H ( jω) in ( jω) We can show S S out in in out ( ( jω) jω) H ( jω) S S in in in in ( jω) H ( jω) * ( jω) in(t) h(t) out(t) and S out out ( jω) H ( jω) S in in ( jω)