Saber Solutions software is composed of addon modules that can be added to the basic package in order to improve software performance (RunTime Modules), to increase the choice of components (components library) and to make more specific analysis (inspecs)
Runtime module
Saber Runtime parallelizes and distributes iterative simulations across computing resources to dramatically improve throughput and reduce simulation time.
The Saber Runtime library works with Saber solutions, Saber Functional Safety Addon, and Saber Inspecs Addon to minimize the time spent running valuable performance and safety simulations. With Saber Runtime, engineering and safety teams can simulate thousands of scenarios that would otherwise have been too costly to perform.
Robust design methodologies require advanced sensitivity and statistical analyses to verify the reliability of complex power networks. These analyses are recursive simulations requiring hundreds or thousands of runs which is impractical to support on a single CPU.
The Saber environment solves this problem by distributing iterated simulations across a compute grid allowing multiple CPUs to perform the analyses in much less time. When a simulation is complete, results are gathered into a single data file for easy processing.
Once the statistical setup is complete, the number of simulation runs required to produce statistically meaningful results must be determined. The number of runs depends on the complexity and characteristics of the design. Simulation runs on the order of several hundred to a few thousand are typically required. This represents a significant decrease in the time it takes to complete the subsystem design.
In conclusion, the RunTime module allows to improve throughput and reduce time with distributed computing. It parallelizes 1000s of simulations for Robust Design, Functional Safety or Inspecs for a maximum efficiency and a considerable savings in development resources and a faster time to market.
Components library
SaberRD has many predefined but you can improve them with a complementary library to enhance your work and performance with an extented manufacturer library of around 30,000 more components than the basic package. Aircraft and automotive design requires accurate model libraries and advanced modeling tools. Saber has the largest library of mixedtechnology models in the industry. Models are available at various levels of abstraction, from highlevel idealized transfer functions to precise, physicsbased devices. These models are optimized for accuracy and performance.
Inspecs
Optional Saber InSpecs to optimize the design for component variations and shifts in operating conditions.
Navigate to :
Monte Carlo
 General Description:Monte Carlo (MC) analysis randomly varies the parameters, within userdefined tolerance ranges, and executes the specified SaberRD analysis at each parameter value. By specifying the tolerance ranges on parts in the design, SaberRD can randomly vary specified parts allowing you to evaluate how the variance of part values in the production environment affects the performance of the design.
The Monte Carlo command executes a set of SaberRD commands in a statistical environment, which has the effect of performing a series of Monte Carlo runs on the commands.
 Comments:
 Saber changes component values every time the loop is executed
 Large or smallsignal analyses allowed
 The user needs also to specify the tolerance of parameters in the design: for example : 10kohms resistor with 10% tolerance: rnom=normal(10k,0.1)
Normal corresponds to the probability density functions.

 MC Analysis results are useful for spotting trends or correlation between a given measure and a component parameter.
As there is a large amount of data, user should use Pareto analysis which allows to rank order the parameters that have the biggest effect on the variance of the design performance measure.

 Pareto results provide correlation data about the impact of tolerance on the design performance measure
Sensitivity (Identify parameters that impact performance)
 General Description:
The sensitivity analysis is used to determine how sensitive a specified design performance measurement varies in design parameters. A performance measurement is a single numeric characteristic of the circuit (e.g. DC voltage, risetime, bandwidth, etc.)
 Required Parameters:
 Parameter List – specifies the design parameters to be varied (perturbed)
 Add Analyses/Measures – specifies which analyses/measures to use after each perturbation
 Comments:
 Saber changes each design parameter by a small amount and calculates the effect on the performance measure
 Tolerance data not used or needed
 Sensitivity calculations:
Where, p is the parameter value and m is the performance measure.
 Sensitivity Report:
After running a sensitivity analysis a report is generated to help identifying the parameters that have the highest influence on the measurements (output ripple voltage for example) : rank parameters by their impact on design performance measure.
Stress
 General Description:
A stress analysis determines the part stress of selected components in the design. Part stress is based on the concept of safe operating areas (SOAs), each of which is a boundary for a particular property of the component. As an example, as the power dissipated in a component cannot exceed a certain rated value, the stress analysis determines the percentage of the actual power dissipation compared to the power rating.
Designers use stress to understand the part stress in their design. It is done based on largesignal analysis. The results of the stress report provide information about how to select parts used in the design.
 Comments:
Users need to specify the stress ratings for each component into the design and also enable the Stress options from Transient simulation settings.
After adding stress measures and successful completion of Transient/Stress analysis, Stress Report is generated as shown below. Stress Report specify the list of components in the ascending order of stress observed.
From the Stress Report, it is observed that:
 All devices except boost diodes are stressed below 50%.
 The voltage across boost diodes are 62% of its rated voltage 650V.
Therefore, Schottky diodes with higher PIV (peak inverse voltage), for example 800V with low forward voltage drop are recommended for this type of application.
Worstcase
 General Description:
The SaberRD WorstCase Analysis (WCA) tool is used to predict system reliability by determining the combination of allowable operating conditions under which an electronic or a mechtronic design will reach its worstcase limits of performance. The tool provides the user the ability to interactively set up, execute, and monitor tests that search for worstcase extremes, visualize results, annotate results back to the design, and output analysis data suitable for reporting and documentation.
Moreover, WCA is essential in faultintolerant and safetycritical systems.
Also, subsystems are complex, so the question is: how do you determine the minimums and maximums of important parameter values?
The WorstCase Analysis tool is a framework providing functions to analyze the effects of design parameters defined over a permissible domain. It helps to identify the most critical designbehavior and design parameters by applying optimization algorithms
These functions are useful to:
 Validate the performance of a design.
 Identify the range attained by design performances of interest when stochastic parameters are varied within their tolerance range (worstcase analysis).
 Identify combinations of design parameters leading to best design performance (design optimization).
 Characterize parameters in order to best fit measured system component behavior (model characterization)
The SaberRD WorstCase Analysis (WCA) Tool is fully integrated with the SaberRD design and verification environment.
WorstCase Analysis Tool WCA Tool is displayed below :
As shown in the picture above :
 WCA tool calculates WC limits
 Significant deviation from nominal behavior (27.9V)
 Parameters could be extracted automatically
 Comments:
Purpose is to overcome the limitations of traditional methods
 Build confidence in worst case results
 Easytouse
 Traditional methods have no guarantee to uncover worstcase behavior and Extreme cases may not be touched for certain designs contrary to WCA.
Salient features
 Flexible definition of WCA objectives (e.g. Min, Max)
 Reuse of existing design set up (e.g. MC tolerances)
 Intuitive graphical user interface
Search Algorithms
 Powerful local & global algorithms
 Combined search possible to leverage synergy of different methods
 Customized calibration possible
Test definition
 Intuitive drag & drop solution
 Analysis Definition
 Measurements & objectives
 Multi objective definition
Functional Safety
Fault analysis
Fault => Investigation of faults in context to function safety (support of ISO 26262).
 General Description:
Fault analysis allows the user to define various fault conditions that a design could meet and to utilize the simulator to automatically step through them and analyze the behavior of the system for each fault condition.
You first define one or more faults for the design and then specify the analysis to perform for each fault. The specification of these analyses is similar to the body of other loop analysis, such as Monte Carlo and parameter sweep.
The benefits of this funcitonal safety analysis are :
 Reduce costly testbench fault experiments
 Improve time to compliance
 Simulate fault scenario that could not be tested before
 Comments:
This kind of analysis help to understand the impact of faults on system behavior and safety. You will be able to apply multiple faults to a system and test for on going operation in a safe range.
 Process:
Fault analysis can be run using the command line or the GUI.
After running the Fault analysis, The Pass/Fail report generated by the Experiment assists designers to interpret impact of faults on the design and take necessary actions on components affecting the design robustness.