eldo_user

Setting Up An Analysis
Statistical and Sensitivity Related Analyses
Statistical and Sensitivity Related Analyses
The statistical and sensitivity related analyses are listed here.
Monte Carlo Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265
Worst Case Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267
DC Mismatch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269
DC Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 270
Transient Sensitivity Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272
AC Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272
Sensitivity Analysis of Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273
Design of Experiments Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274
Monte Carlo Analysis
Monte Carlo analysis (.MC command) determines the uncertainty in estimates for dependent
variables of interest. It focuses on data and how uncertainty in data propagates through
computations.
Eldo reads a netlist describing the circuit and translates it into mathematical equations. The
inputs are the various design parameters, the process parameters, and the environmental
conditions. The output space is characterized by the circuit performance of interest.
Monte Carlo-based uncertainty analysis is performed on multiple model evaluations with
randomly selected model input variables. The results of these evaluations (experiments) are
then used to determine the uncertainty in model predictions, and the input variables that gave
rise to this uncertainty.
In statistical models for transistor level simulation, there are two types of variability in device
properties:
• Inter-die (between chips) variations or global variations (LOT keyword).
Typical examples of statistical parameters with global tolerances are oxide thickness or
channel length reduction.
• Intra-die (within chip) variations are local variations that affect transistors individually
(DEV keyword).
A typical example of statistical parameters with local tolerances are the transistor
threshold voltages.
The overall Monte Carlo analysis run is made of several chunks of simulations. Using an
incremental approach provides an insight into the spread of distribution. You can also
extrapolate to obtain the general shape of the distribution. In Eldo, you control the sample size
Eldo® User's Manual, 15.3 265