Bootstrapping and miller's theorem

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In summary, the input impedance of a bootstrapped darlington emitter follower is increased by using Miller's theorem. The Miller capacitance between base and collector provides negative feedback, while bootstrapping provides positive unity feedback from output to input. This is commonly used to eliminate the capacitance in a coax or triax cable by driving the shield with a buffered version of the signal. A resistor can be used to find the input resistance, or Miller's theorem can be used to calculate it. This technique is also known as the Miller effect.
  • #1
ShreyasR
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I am trying to understand how the input impedance of a bootstrapped darlington emitter follower is analysed. They make use of Miller's theorem. But honestly, I have not understood this theorem as well... All I know is bootstapping increases input impedance but i have no clue as to how? And why is a capacitor connected between the input and output terminals? Can someone link me to some place where a bootstrapped darlington circuit is analysed? I googled it but did not find any helpful links...
 
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  • #2
ShreyasR said:
I am trying to understand how the input impedance of a bootstrapped darlington emitter follower is analysed. They make use of Miller's theorem. But honestly, I have not understood this theorem as well... All I know is bootstapping increases input impedance but i have no clue as to how? And why is a capacitor connected between the input and output terminals? Can someone link me to some place where a bootstrapped darlington circuit is analysed? I googled it but did not find any helpful links...

IIRC, the Miller capacitance is between base and collector, and provides negative feedback (slowing down frequency response). Bootstrapping is positive unity feedback from output to input (for any amplifier) to increase input impedance, so it would be between emitter and base in this case.

The most common bootstrapping I'm familiar with is bootstrapping the shield capacitance on an input to an amplifier. If the input coax cable (or triax cable) has enough capacitance to cause a LPF rolloff of the input signal, you can drive the shield with a buffered version of the signal to eliminate the capacitance to the adjacent shield.

http://www.ti.com/general/docs/lit/getliterature.tsp?literatureNumber=snoa664&fileType=pdf

.
 
  • #3
Hi, Miller's theorem is better know as a Miller effect

http://en.wikipedia.org/wiki/Miller_effect
http://web.mit.edu/klund/www/papers/jmiller.pdf

We simply have a ideal voltage amplifier with gain equal to A = -10V/V

Next we connect a resistor R = 10Ω between the input and the output of the amplifier.

attachment.php?attachmentid=64300&stc=1&d=1385502637.png


Now let as try to find a input resistance.

Rin = Vin/Iin

In = (Vin - Vout)/R = (Vin - A*Vin)/R = Vin * (1 - A)/R

Rin = Vin/Iin = R/(1 - A)

Iin = (1V - (-10V))/10Ω = 1.1A

So Rin = 1V/1.1A = 0.909Ω

So as you can see our Rin resistance is (1 - A) smaller then R if we have inverting amplifier .
And this is what we call a Miller effect

Now let us consider different situation. We replace our amplifier with "voltage follower" amplifier.
But now the gain is equal to A = 0.5V/V

So if Vin = 1V we get 0.5V at the output. So the input current is equal to:

Iin = (Vin - Vout)/R = 0.5V/10Ω = 50mA and therefore

Rin = 1V/50mA = 20Ω

If we increase the gain to 0.9V/V we have

Iin = (Vin - Vout)/R = 0.1V/10Ω = 10mA

So the input resistance is equal to

Rin = 1V/10mA = 100Ω


attachment.php?attachmentid=64301&stc=1&d=1385504371.png


Also we can use Miller's theorem to find Rin.

Rin = R/(1 - A) = 10Ω/(1 - 0.5) = 20Ω

Rin = R/(1 - A) = 10Ω/(1 - 0.9) = 100Ω


But this time we call this bootstrap
http://electronics.stackexchange.co...analysis-of-a-emitter-follower-with-bootstrap

Any questions ?
 

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Related to Bootstrapping and miller's theorem

1. What is bootstrapping in statistics?

Bootstrapping in statistics is a resampling technique used to estimate the sampling distribution of a statistic by taking repeated samples from the original data set. It is often used when the assumptions of traditional statistical tests cannot be met or when the sample size is small. Bootstrapping involves randomly selecting data points from the original sample with replacement, creating a new sample of the same size as the original. This process is repeated multiple times to generate a distribution of the statistic of interest.

2. How does bootstrapping differ from traditional statistical methods?

Bootstrapping differs from traditional statistical methods in that it does not rely on assumptions about the underlying population distribution. Instead, it uses the data itself to estimate the sampling distribution of a statistic. This makes it a more robust and flexible method, especially when dealing with non-normal or small sample data.

3. What is the purpose of bootstrapping?

The purpose of bootstrapping is to generate a more accurate estimate of the sampling distribution of a statistic. This can be useful in situations where traditional statistical methods may not be appropriate, or when sample sizes are small. Bootstrapping can also be used to calculate confidence intervals and assess the variability of a statistic.

4. What is Miller's theorem in statistics?

Miller's theorem, also known as the central limit theorem for bootstrapping, states that as the number of bootstrap samples approaches infinity, the sampling distribution of a statistic will approach a normal distribution. This means that even if the original data does not follow a normal distribution, the sampling distribution of a bootstrap statistic will become more and more normal as the number of bootstrap samples increases.

5. How can bootstrapping be used in hypothesis testing?

Bootstrapping can be used in hypothesis testing by generating a null distribution of a statistic under the assumption that the null hypothesis is true. This distribution can then be compared to the observed statistic to calculate a p-value. Bootstrapping can also be used to create confidence intervals for the difference between two groups or to compare the means of two groups. It is a useful tool when the assumptions of traditional hypothesis testing methods cannot be met.

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