Application Note
POCV/AOCV data generation with
GoldChar©
1. Introduction
In order to ensure the proper design operation across the variation space, timing verification has to be
performed on all relevant combination of process and environmental parameters. The need for accurately
capturing the process variations on the design side has led to investigation and deployment of new
analysis techniques, such as On Chip Variations (OCV) for within-die (local) variations, and Corner
Analysis for die-to-die (global) variations.
Advanced OCV (OCV) is an enhanced version of the prior OCV techniques to include stage based and/or
location based within-die variations. Location based effects tend to be more systematic in nature and
usually are not addressed using AOCV techniques. In order to account for the stage effect in the timing
analysis, the AOCV technique requires the generation of stage based derate tables which include delay
derates at various number of stages. Parametric OCV (POCV) comes as an enhancement to AOCV by
removing hassles and heuristics associated with the AOCV table generation process. The required inputs
to POCV analysis are only the cell variations. POCV reduces the pessimism and increases the design
robustness compared to traditional AOCV approaches by accounting for the on-chip variation effects
within a statistical timing analysis framework.
GoldChar is a characterization tool targeted for AOCV table and POCV data generation. The advantage
of using GoldChar is that in the path based analysis, the AOCV results match to POCV results almost
perfectly.
2. Basic GoldChar Usage
GoldChar can be launched from the command line by typing goldchar <input_file>. It requires a simple
input file to setup the characterization process as shown in Example 1 below.
#reading library
read_lib test.lib
#read spice netlist and model
read_spice -netlist netlists/all_circuits.sp
read_spice -model models/header.mod
# run characterization
char_ocv -from tsmc40 -to ocv -param {parl1 par2} –cell * -dir spice_dir
# output pocv format
write_ocv ocv pocv.data.char -format pocv
# output aocv format
write_ocv ocv aocv.table.char -format aocv
Example 1. GoldChar Input File
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As seen in Example 1, the tool needs a cell library file (ccs or nldm) to determine cells and slew load
points for the cells. Then extracted cell netlists and the statistical model files which include local
variations are required. char_ocv is the primary command which does data generation for both AOCV
and POCV. It uses an internal spice simulator called pcsim to run spice decks generated for
characterization. In this case the data is generated –from tsmc40 library –to ocv library, with two local
variation parameters parl1 and parl2. –cell option specifies the cells for the characterization, and the –dir
specifies the simulation directory. If the simulation directory already contains the required data, GoldChar
uses the existing data without running the simulations. write_ocv command is used to write out the
characterization data either in POCV or in AOCV format.
The options of char_ocv command are listed below
char_ocv [options]
-from :
-to :
-param :
-cell :
-dir :
-write char :
-write monte :
-mc :
-rise_slew
-rise_load
-fall_slew
-fall_load
:
:
:
:
library name
a new library which contains POCV and AOCV data
local process parameters to be used
in characterization
name of the cell, wild chart (*) is supported
directory for spice simulations
write spice decks for statistical characterization to
the spice directory, does not
run the characterization
write spice decks for Monte Carlo characterization to
the spice directory, does not
run the characterization
runs Monte Carlo characterization,
generates POCV/AOCV data
user defined rise slew
user defined rise load
user defined fall slew
user defined fall load
If the user does not specify any specific slew/load combination for the characterization, the middle
slew/load values from the table will be used by default. Please note that having –write option would
disable any spice run for data generation, with this option char_ocv only generates the spice decks under
the spice directory. After reading the libs, cell netlists and models, below are different ways or running
char_ocv
1. Generating Monte Carlo spice decks under spice_dir1, no spice run:
char_ocv -from tsmc40 -to ocv -param {parl1 par2} –cell * -dir spice_dir1 –write monte
2. Generating statistical characterization spice decks under spice_dir2, no spice run:
char_ocv -from tsmc40 -to ocv -param {parl1 par2} –cell * -dir spice_dir2 –write char
3. Running and generating char data for AOCV/POCV using statistical characterization. The spice
decks and output files are under spice_dir3.
char_ocv -from tsmc40 -to ocv -param {parl1 par2} –cell * -dir spice_dir3
write_ocv ocv aocv.table.char –format aocv
write_ocv ocv pocv.data.char –format pocv
4. Running and generating char data for AOCV/POCV using Monte Carlo simulation. The spice decks
and output files are under spice_dir4.
char_ocv -from tsmc40 -to ocv -param {parl1 par2} –cell * -dir spice_dir3 -mc
write_ocv ocv aocv.table.mc –format aocv
write_ocv ocv pocv.data.mc –format pocv
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Once char_ocv is completed the data has to be written to a file using write_ocv command.
write_ocv <lib_name> <output_file> [options]
-format :
output file format, can be either aocv or pocv
-stages :
number of stages in AOCV table
write_ocv supports two format options, pocv or aocv. Both AOCV tables and POCV cell variation data is
generated for all arcs in the cell.
AOCV table for a cell (CKINVX) would look like as in Example 2.
# Apply-Cell
Cell CKINVX
Arc i-o
RISE
Coefficient 0.110211
Stage
0.110211 0.220422 0.440844 0.881687 1.76337 3.52675 7.0535 14.107 28.214 56.428
Early: 0.978297 0.984653 0.989148 0.992327 0.994574 0.996163 0.997287 0.998082 0.998644 0.999041
Late: 1.0217 1.01535 1.01085 1.00767 1.00543 1.00384 1.00271 1.00192 1.00136 1.00096
Arc i-o
FALL
Coefficient 0.241636
Stage
0.241636 0.483272 0.966544 1.93309 3.86618 7.73235 15.4647 30.9294 61.8588 123.718
Early: 0.967445 0.97698 0.983723 0.98849 0.991861 0.994245 0.995931 0.997123 0.997965 0.998561
Late: 1.03255 1.02302 1.01628 1.01151 1.00814 1.00575 1.00407 1.00288 1.00203 1.00144
Example 2. GoldChar AOCV table output (aocv.table.char)
The Early and Late derate information is provided for each arc. Stage axis denotes effective stage count
for an accurate AOCV analysis. Coefficient is the value for calculating the effective stage count.
For the same cell, POCV data is given in Example 3.
Apply-Cell
Cell CKINVX
Arc i-o
RISE
0.00723449
Arc i-o
FALL
0.0108516
Example 3. GoldChar POCV output (pocv.data.char)
POCV analysis requires the percent rise/fall delay variation of each cell or arc. In Example 3, the percent
delay variations are given for arc i-o, 0.723% for rise delay, and 1.08% for fall delay.
3. Parsing GoldChar output files for GoldTime Usage
GoldChar generates arc level AOCV and POCV data including “when” conditions in library. Say, the
same arc with 5 different when conditions would result in 5 Early/Late tables for rise, and another 5 tables
for fall. GoldTime currently does not support arc-level AOCV tables. Therefore the data needs to be
parsed to convert the tables in GT readable format using goldchar2aocv script.
Usage:
goldchar2aocv [options]
-char <GoldChar AOCV table>
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Running this script on the aocv.table.char file generated in Example 2, would produce three different files:
aocv.table.char.early : contains early derate tables for the cell
aocv.table.char.late : contains late derate tables for the cell
aocv.table.char.tcl : contains stage count adjustment coefficient for the cell
In Example 4, we show all these files for the GoldChar generated AOCV table given in Example 2.
Apply-Cell
Early-fall
Cell LPS_CKINVX36
Stage 0.2416 0.4833 0.9665 1.9331 3.8662 7.7324 15.4647 30.9294 61.8588 123.7180
Distance 0
0.9674 0.9770 0.9837 0.9885 0.9919 0.9942 0.9959 0.9971 0.9980 0.9986
Apply-Cell
Early-rise
Cell LPS_CKINVX36
Stage 0.1102 0.2204 0.4408 0.8817 1.7634 3.5267 7.0535 14.1070 28.2140 56.4280
Distance 0
0.9783 0.9847 0.9891 0.9923 0.9946 0.9962 0.9973 0.9981 0.9986 0.9990
Apply-Cell
Late-fall
Cell LPS_CKINVX36
Stage 0.2416 0.4833 0.9665 1.9331 3.8662 7.7324 15.4647 30.9294 61.8588 123.7180
Distance 0
1.0326 1.0230 1.0163 1.0115 1.0081 1.0057 1.0041 1.0029 1.0020 1.0014
Apply-Cell
Late-rise
Cell LPS_CKINVX36
Stage 0.1102 0.2204 0.4408 0.8817 1.7634 3.5267 7.0535 14.1070 28.2140 56.4280
Distance 0
1.0217 1.0153 1.0109 1.0077 1.0054 1.0038 1.0027 1.0019 1.0014 1.0010
set_locv_coefficient 0.1759 LPS_CKINVX36
Example 4. goldchar2aocv generated GT AOCV input files
Arc-level POCV is supported however the output data still needs to be parsed to GT readable format.
goldchar2pocv script does the parsing.
Usage:
goldchar2pocv [options]
-char <GoldChar POCV table>
Running this script on the pocv.data.char file given in Example 3 would produce pocv.data.char.tcl file
which is given below.
set_cluster -parameter random* -sigma -cell_different_sigma
#Cell CKINVX
set_variation -parameter random_fall_variation -sigma 0.0108516 -from i -to o -lib_cell CKINVX
set_variation -parameter random_rise_variation -sigma 0.00723449 -from i -to o -lib_cell CKINVX
Example 5. goldchar2pocv generated GT POCV input file
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4. Using the AOCV/POCV Data in GoldTime
In order to run AOCV analysis in GT, user needs to read in the three files generated by goldchar2aocv
script before running timing analysis.
read_locv aocv.table.char.early
read_locv aocv.table.char.late
source aocv.table.char.tcl
In the case of POCV, pocv.data.char.tcl file needs to be sourced before running POCV analysis.
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