A practical differentiator has two cut-off frequency but can no longer perform differentiaiton after first cut-off. After first cut-off there is a 20db per decade gain reduction but why does this affect the mathematical function itself. The DC equation still remains
$$
V_{out}=-R_fC_f \frac{dV_{in}}{dt}.
$$
In a low-pass filter the signal just attenuates and does not change mathematically, is something different here.
Is it just that it is still differentiating but for some reason my output is distorted or is there a mathematical explanation behind this. I am using a standard practical differentiator as shown below and I am sure the values I have are correct.
Electronic – Differentiator cut-off frequency
bode plotgainloop-gainmathoperational-amplifier
Related Solutions
The natural resonant frequency of a 2nd order low pass filter will have a pole zero diagram like this: -
If you are unfamiliar with pole-zero diagrams see if this helps: -
If you follow that, then go back to the first diagram and you should realize that the undamped natural resonant frequency (\$\omega_0\$) has a value anywhere on the semi circle and it is the Q of the filter how far round from the jw axis the two poles are. Given that any order of butterworth filter has all its poles on the same semi circle, the answer to your question is: -
Is the cut-off frequency for each high order butterworth filter the same or different?
If you mean the natural resonant frequency then YES!
If you mean the 3dB point of each filter's response on the jw axis (the axis that pertains to "real-life" measurements on a spectrum analyser) then NO!
Butterworth pole-zero diagram: -
The above shows the poles of a 10th order butterworth filter - note that all the poles lie on the same circle and therefore all the individual filters (5 x 2nd order) have the same natural resonant frequency.
Taken from here.
Okay, after a lot more research, I think I've gotten to the bottom of this. Actually I'm certain it's only approaching the bottom, as I've found this topic area quite deep, but I think I've gotten close enough to shed some light.
A basic misconception
A turning point in my understanding was when I realized that the equation I led off with in the OP:
$$ G = \frac{A_o}{1 + A_o\beta} $$
is a block diagram equation, not a circuit equation. Those are two different things and the translation between one and the other is often not trivial. The fact that the translation is trivial for the simple non-inverting op amp case is perhaps a trap for the unwary, certainly one I fell into head first :)
We'll see why that matters shortly.
What is noise gain, really?
Noise gain (in an op amp circuit) is the gain experienced by a small signal applied at the non-inverting (+) input.
It is so called because noise is frequently stated as "referred to the input", meaning the noise signal that would need to be present at the input to produce a specified noise output. This allows noise that originates in various parts of the op amp to be "lumped" into a single equivalent value, simplifying any analysis that doesn't really care where inside the black box the noise originates.
In a simple non-inverting amplifier, noise gain is the same as the signal gain:
That makes sense when you consider that the signal is applied directly to the non-inverting input, and a small differential voltage applied at that node would experience precisely the same gain as the signal does.
I think a block diagram view shows this most clearly. It's probably not strictly necessary for understanding this simple non-inverting case, but I found it crucial for understanding the general case. Also, \$\beta\$ is a block diagram variable, so we can avoid repeated mental block-to-circuit translations if we stay in the block diagram domain when we use it for reasoning.
The \$+\$ node on the summing block corresponds to the non-inverting input of the op amp (in this case, but not in general as we'll see below). It's easy to see there's no difference between a noise signal and a "real" signal applied there and the noise gain in this case is:
$$ NG = \frac{A_o}{1 + A_o\beta} $$
Now, in his book, Walter Jung defines noise gain as \$1/\beta\$. And, I expect we all know that the equation just above is approximately \$1/\beta\$ when \$A_o\beta \gg 1\$. In fact, this approximation is great for getting the DC noise gain, the Y-axis position of the long flat part at the beginning of its magnitude curve on the Bode chart. But if you want to see its frequency dependent behavior (for example, to plot it in SPICE), you need to use the long form.
Ok, so we're well on our way to how to calculate noise gain in the general case, but one challenge remains: How do we determine the value of beta (\$\beta\$)? It might not be obvious at first, but this is a challenge because the components that contribute to beta may also contribute to other blocks. There's no guarantee the feedback network has them all to itself; in fact, we need look no further than the inverting amplifier configuration for an example of them being "shared" (perhaps more precisely, interdependent).
Consider the inverting amplifier circuit below:
The block diagram for this circuit turns out to be this:
I won't go through the details of how you get here from the circuit diagram, but that might make an interesting follow-on question if you wanted to post it. Basically you create a Thevenin equivalent looking into \$R_f\$ from the inverting terminal and then use superposition to get the two contributions to the summing node. Note that here, \$V_e\$ represents \$V_- - V_+\$ at the op amp inputs, which is why \$A_o\$ and \$\beta\$ have minus signs in their expressions.
There are a couple interesting things we can see:
The input signal \$v_{in}\$ does not appear directly at the summing node. It is first attenuated by \$T_i\$ (\$T_i\$ here stands for input transmittance). This explains why the noise gain is not equal to the signal gain for the inverting topology. Noise gain is an attribute of the core amplifier loop, not the overall circuit.
\$\beta\$ is the same as it is for the non-inverting case (once you get the signs sorted out). This explains why the noise gain is the same for the inverting and non-inverting topologies.
\$R_f\$ and \$R_{in}\$ appear in both the \$\beta\$ and \$T_i\$ block expressions. This reflects the interdependence between the feedback network and the input attenuation network. Changing one of the impedances therefore changes both the signal and the noise gain. So it's not possible to modify them separately by changing values of the existing feedback network components.
So what is "forcing the noise gain" and why does it work?
I got into this question of noise gain pursuing an interest in op amp stability/compensation, not noise. I found a couple references that claimed (paraphrased) "... forcing the noise gain is a powerful compensation technique that many analog engineers don't know about ...". My reaction was: "Hmm, sounds interesting! I love the analog black arts! What's noise gain? And how do I force it to do something it doesn't want to?"
Well, after this recent research, I'm inclined to think "forcing the loop gain" (downward) is a more apt expression, since that is what enhances the stability. The loop gain is \$A_o \beta\$; changing \$\beta\$ isn't the only way to change that product. This will become more clear in a minute.
As a reminder, this is what the "forced noise gain" circuit from above looks like, as applied to a non-inverting amplifier:
If we do the same Thevenin equivalent analysis to isolate the feedback and input blocks, we end up with a block diagram that looks like this:
We can observe a few interesting points:
The feedback path is attenuated by \$T_f\$. This effectively reduces the feedback fraction, increasing the closed loop gain of the core amplifier loop, otherwise known as the noise gain.
The input is attenuated by \$T_i\$, which is exactly the same as \$T_f\$. This normally would have the effect of decreasing the overall circuit signal gain. However, in this case, that decrease is exactly offset by the increase in noise gain and overall signal gain is unaffected.
Because \$T_i\$ and \$T_f\$ are the same and because they both appear immediately prior to a summing block, block diagram algebra allows us to move that block to the other side of the summer as in the figure below. Just a caution though, while manipulations of the block diagram like this still give you the right answer for the overall transfer function \$V_{out}/V_{in}\$, the correspondence of any given signal (connecting line) to a physical point on the circuit can be disrupted.
Embracing the equivalent diagram this gives us, we see that the desired reduction in loop gain can be achieved by attenuating the gain of the main amplifier, without producing a change in the overall signal gain (at low frequencies).
There is a really excellent video development of this by the late Professor James Roberge of MIT (starting about 35:17). I ended up watching the whole 20 lecture series (most of it twice :) and highly recommend it :)
I also worked out how to directly plot the noise gain in LTspice, I've posted that as a follow-up question if you'd like to take a look: How do I plot noise gain of an op amp circuit in SPICE?.
Best Answer
Since you are talking about a non-ideal case, then you can no longer use the ideal transfer function. What you have shown has this transfer function:
$$\begin{align} Z_f&=\frac{1}{\frac{1}{R_f}+sC_f} \\ Z_1&=R_1+\frac{1}{sC_1} \\ H(s)&=\frac{Z_f}{Z_1} \\ H(s)&=\frac{1}{\left(R_1+\frac{1}{sC_1}\right)\left(R_f+\frac{1}{sC_f}\right)} \\ H(s)&=\frac{\frac{s}{R_1C_f}}{s^2+\frac{R_fC_f+R_1C_1}{R_fR_1C_fC_1}s+\frac{1}{R_fR_1C_fC_1}} \end{align}$$
So you can see that it's a bandpass now, which matches the frequency plot in your second picture. Even if you have this transfer function, you cannot talk about a corner frequency for a differentiator, since, if there is a pole, it's no longer a differentiator, it's a highpass, at least. And things get a bit more complicated due to the internal pole of the opamp, parasitic indictances of the elements, themselves, but for a general (and, usually, whithin the necesssary bandwidth) case, this will do, and you can call it a differentiator.