The TransistoriZed logo should be here! But... curses! Your browser does not support SVG!

homelist of postsdocsγ ~> e-about & FAQ


Offset measurement of latched comparators.

During comparator design one is often (but not always) interested in the systematic and mismatch-related offset. When dealing with continuous-time open-loop comparators the offset measurement methodology is similar conventional OTA offset measurement e.g. DC sweep, while latched comparators require a somewhat more discrete-like manner of measuring offset.

One possible approach is to use a very precise (long sloped) linear ramp, and using massive oversampling and comparator decision storage. This approach seems to be a bit tedious as its precision is solely dependent on the ramp's slope.

A basic illustration of the linear ramp method.
A basic illustration of the linear ramp method.

An alternative method for measuring offset of latched comparators would be the binary search method. One can possibly use an ideal DAC to apply "search" voltages and depending on the comparator's decision reach a settling value with a constant binary-weighted precision. E.g. to achieve 16 bits of search accuracy only 16 cycles would be required. Here are some basic illustrative examples of the two methods.

An illustration of the binary search method.
An illustration of the binary search method.
The binary search method for measurement can be applied in various ways. One practical implementation was to implement the algorithm using Verilog-A and directly add a "search" instance where you can test your comparator directly in your existing SPICE simulation. Here is a very simple example of a possible implementation.

// VerilogA for daisyCycAd, daisyCycAdBinSchSAR, veriloga
//
// A component handy in comparator offset measurement. Uses a binary search algorithm with a "dive" coefficient of 2. See comments for more information.
//
// Initial version P1A - Deyan Dimitrov didolevski@gmail.com
//


`include "constants.vams"
`include "disciplines.vams"

module daisyCycAdBinSchSAR(vCompIn, vCompOut, vClk, vdd, gnd, vCompRef);

input vCompIn, vClk;

output vCompOut, vCompRef;

inout vdd, gnd;

electrical vCompIn, vCompOut, vClk, vdd, gnd, vCompRef;

parameter real vTransClk = 1.65;
parameter real vTransComp = 1.65;
parameter real vSchTop = 3.3;
parameter real vSchBot = 0;
parameter real vCompReference = 1.5;
//parameter integer Resolution = 16;
parameter integer NrOfCodes = 65535;
//parameter real tCompSpeed = 100e-12;
real vRefInReal;
real vBinSch;
real vComp;
real scharray[0:NrOfCodes];
//real next;
integer imid;
integer imin;
integer imax;
integer i;
integer codes;

analog begin

       @(initial_step)
       begin

       vBinSch = vSchBot + (vSchTop-vSchBot)/2;  // Mid point as a start of the search

       imin = 0;
       imax = NrOfCodes-2;
       imid = (NrOfCodes-2)/2;

       	   for (i = 0; i < NrOfCodes-1; i = i + 1) begin
       	     scharray[i] = (((vSchTop-vSchBot)/NrOfCodes)*i)+vSchBot;   // Creating reference array
      	   end

	   $strobe("Array Max %g", scharray[imax]);
	   $strobe("Array Min %g", scharray[imin]);

       end

       @(cross(V(vClk) - vTransClk, 1)) begin

//       next = $abstime + tCompSpeed;    // Possible internal compensation for comparator's delay
//       end
//       @(timer(next)) begin
   
//       $strobe("Imax: %d", imax);
//       $strobe("Imid: %d", imid);
//       $strobe("Imin: %d", imin);

       vComp = V(vCompIn);   // Strobe comparator decision
//       vRefInReal = V(vRefIn, gnd);

       if (imax >= imin) begin	// Continue searching if imax >= imin

       	  if (V(vCompIn) > vTransComp) begin      // Find-out which sub-array to search
       	  $strobe("%g",vComp);
//       	  vBinSch = vBinSch + (vSchTop-vBinSch)/2;
		  imin = imid + 1;	          // Change min index for the upper sub-array
		  imid = imin + ((imax-imin)/2);  // Update imid to be used for strobing-out
		  end
          else 	  
              begin
          $strobe("%g",vComp);
//       	  vBinSch = vBinSch - (vBinSch-vSchBot)/2;
		  imax = imid - 1;		  // Change max index for the upper sub-array
		  imid = imin + ((imax-imin)/2);  // Update imid to be used for strobing-out
              end

          end         

       vBinSch = scharray[imid];		  // Look-up at the reference array and assign to vBinSch (Votage to be strobed-out)
       end

       V(vCompOut) <+ vBinSch;			  // Update search voltage
       V(vCompRef) <+ vCompReference; 		  // Update comparator reference

end

endmodule

The whole principle is simple and self-explanatory. Some delay between the latch clock and S/H clock of the search component is required to compensate for the comparator's speed. Here are some practical usage illustrations.
A primitive testbench proposal.
A primitive testbench proposal.
Component settings.
Component settings.
Offset search progress.
Offset search progress.

If you find this useful the component's symbol for Virtuoso 6 as well as the *.va code can be found here.

Date:Sun Mar 11 12:46:50 CEST 2014

LEAVE A COMMENT | SEE OTHER COMMENTS



A basic "seed" for plotting csv data files with matplotlib.

Well, basically all the usage guide and information about the matplotlib library is available online, however I am writing this as an emphasis on how good matplotlib actually is, moreover I can again use this short snippet as a quick "seed/template" in the future without having to write it from scratch. Here's a short example on plotting line charts from a *.csv file.

## Plotting from a csv and writing down a pdf using matplotlib. Look after the transpose function for the column plotting settings.
## 
## Initial verison A Deyan Levski - didolevski@gmail.com
##
##

from numpy import array 
import csv 
import matplotlib.pyplot as plt 
 
#def plotit(filename,title): 

filename='Slew-Rate-Limit-Errors-Capture.csv';
title='OTA residue voltage';
 
cs=csv.reader(file(filename,"r"),delimiter=",")# seperator of csv data is a tab ("\t") 
# Have to convert comma decimal to dot decimal delimiter 
# newlist=[[float(e.replace(".",".")) for e in x] for x in cs] 
#If no conversion needed paste in: newlist=[[float(e) for e in x] for x in cs] 
newlist=[[float(e) for e in x] for x in cs]
# convert from  pairs for [[x1,y1],[x2,y2]...etc] to [x1,x2,..] [y1,y2,..]

ar=array(newlist).transpose() 

fig=plt.figure() 

ax=fig.add_subplot(1,1,1) 
ax.plot(ar[0],ar[1]) 
ax.plot(ar[0],ar[3])
ax.set_xlabel(r"$T_{p}$") 
ax.set_ylabel(r"Voltage")

ax.grid(True)  # Default grid settings
#ax.grid(color='b', alpha=0.5, linestyle='dashed', linewidth=0.5)

#ax.axis('tight') # Tight auto axis

ax.set_ylim([-0.5, 4]) # Custom Y axis
#axes[2].set_xlim([2, 5]) # Custom X axis

plt.title(title) # Plot
	
plt.savefig(filename+".pdf") # Save figure

One can also use it for plotting histograms, e.g. if you are running some MonteCarlo simulations directly from the SPICE engine and you do not have any GUI available to use.

## Plotting from a csv and writing down a pdf using matplotlib. Look after the transpose function for the column plotting settings.
## 
## Initial verison A Deyan Levski - didolevski@gmail.com
##
##

from matplotlib import rc
rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
## for Palatino and other serif fonts use:
#rc('font',**{'family':'serif','serif':['Palatino']})
rc('text', usetex=True)

from numpy import array 
import csv
import math 
import numpy as np
import matplotlib.pyplot as plt 
import matplotlib.mlab as mlab
 
#def plotit(filename,title): 

filename='OTA-InputOffset-MC-4000.hash';
title='OTA Input Offset Distribution';
 
cs=csv.reader(file(filename,"r"),delimiter=",")# seperator of csv data is a tab ("\t") 
# Have to convert comma decimal to dot decimal delimiter 
# newlist=[[float(e.replace(".",".")) for e in x] for x in cs] 
#If no conversion needed paste in: newlist=[[float(e) for e in x] for x in cs] 
newlist=[[float(e) for e in x] for x in cs]
# convert from  pairs for [[x1,y1],[x2,y2]...etc] to [x1,x2,..] [y1,y2,..]

ar=array(newlist).transpose() 

fig=plt.figure() 

ax = fig.add_subplot(111)

# the histogram of the data
n, bins, patches = ax.hist(ar[1], 30, normed=1, facecolor='green', alpha=0.75)
# hist uses np.histogram under the hood to create 'n' and 'bins'.
# np.histogram returns the bin edges, so there will be 50 probability
# density values in n, 51 bin edges in bins and 50 patches.  To get
# everything lined up, we'll compute the bin centers
bincenters = 0.5*(bins[1:]+bins[:-1])

acc=0
nr=0
for i in ar[1]:
  acc += i
  nr += 1

mu=acc/nr
print mu

acc2=0
nr=0
for i in ar[1]:
  acc += ((i-mu)**2)
  nr += 1

sd=acc/nr
sd=math.sqrt(abs(sd))
print sd

# add a 'best fit' line for the normal PDF
y = mlab.normpdf( bincenters, mu, sd)
l = ax.plot(bincenters, y, 'r--', linewidth=1)

ax.set_xlabel('Input offset voltage')
ax.set_ylabel('Run')

ax.set_xlim(-0.05,0.05)
#ax.set_ylim(0, 0.03)
ax.grid(True)
plt.title(r'Input offset voltage variation $\mu$=%.3f, $\sigma$=%.3f' %(mu, sd))
plt.savefig(filename+".ps") # Save figure 
plt.show()

#ax=fig.add_subplot(1,1,1) 
#ax.plot(ar[0],ar[1]) 
#ax.plot(ar[0],ar[3])
#ax.set_xlabel(r"$T_{p}$") 
#ax.set_ylabel(r"Voltage")

#ax.grid(True)  # Default grid settings
#ax.grid(color='b', alpha=0.5, linestyle='dashed', linewidth=0.5)

#ax.axis('tight') # Tight auto axis

#ax.set_ylim([-0.5, 4]) # Custom Y axis
#axes[2].set_xlim([2, 5]) # Custom X axis

#plt.title(title) # Plot
	
#plt.savefig(filename+".pdf") # Save figure 

Here is a link to the "seed" folder. Where generated pdf graps can also be found.

Date:Sun Mar 09 12:46:50 CEST 2014

LEAVE A COMMENT | SEE OTHER COMMENTS



INL and CFPN image artifact estimation script.

A short function aiming to give a rough overview of INL and column mismatch effects on images in a column-parallel ADCs in CMOS Image Sensors. For now it works only with 8 bit images. Some bugs in the file read functions, not supporting all image formats could be existent.

Here are some examples of images with various INL and column mismatch:

Column mismatch 4 LSB

Column mismatch of 4 LSB.

Column mismatch 8 LSB

Column mismatch of 8 LSB.

An attempt to model randomly distributed INL was also made, however in practice ADC INL is correlated and will not necessarily be so random. Basically random INL would appear as random noise on the image. Anyway it is a good example of what would happen if theoretically we have randomly distributed INL.

A random uniform distributed INL (code vs INL in LSBs).

A random uniform distributed INL (code vs INL in LSBs).

Random INL +- 1 LSB.

Random INL +- 1 LSB.

Random INL +- 16 LSB.

Random INL +- 16 LSB.

You can try-out the script it is available here. It is also available at Matlab Central.

Date:Sun Mar 07 12:46:50 CEST 2014

LEAVE A COMMENT | SEE OTHER COMMENTS



Releasing the C source of the AWS.

Lately some nostalgy on the old plenty-of-time days has caught me. I was randomly looking through the pages, being in the process of updating and adding-up old work when I came to one of the not-very-latest versions of the source code of the AWS. I realize that I have not posted it, it might be that someone could actually benefit from it...or?

Anyway, you can find it here. You can also find the board and schematics here.

Date:Sun Mar 06 12:46:50 CEST 2014

LEAVE A COMMENT | SEE OTHER COMMENTS