What Is WCCA, Worst Case Circuit Analysis, With Example, Purpose

June 11, 2022June 11, 2022 CS Electrical And Electronics

Hello guys, welcome back to our blog. Here in this article, we will be discussing what is WCCA or worst-case circuit analysis, how to do a worst-case analysis of components, types of methodology, benefits, and disadvantages of worst-case circuit analysis.

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What Is WCCA, Worst Case Circuit Analysis

Worst case circuit analysis (WCCA) is a process that, by accounting for detail variability, defines circuit rendition under a worst-case design, i.e., under severe environmental or working conditions. Environmental prerequisites are defined as exterior stresses involved in each circuit element and can have a temperature, humidity, or radiation.

Working requirements contain exterior electrical inputs, but must also evaluate factors such as element grade level, the relations between parts, and drift due to element aging. The output of a WCCA permits an estimate of actually used part stresses against rated part parameters. This can assist provide sufficient part stress derating to meet design needs. WCCA should be assessed for all circuitry that is safe and/or financially required.

Implementation of a WCCA, and performance of its results, can support identifying design problems other than that can facilitate financial, legal, and security hazards to the factory and help provide satisfactory arrangements for the customer beneath virtually all operating requirements.

One of the most critical steps concerned with achieving a significant WCCA is the development of a feature characteristic database. This database includes a composite of knowledge required for quantifying sources of element parameter variation. Once these sources have been determined, the database can be utilized to estimate worst-case component importance for essential parameters.

Quantifying the assistance of environmental impacts on component variability (as will be described in an example) is even a necessary step in the evolution of a WCCA. A number of beginning places can be utilized to show random and biased assistance to variability.

They may be translated as:

Enterprise data (historical test data from different products, or certain test programs)
Dealer data (documentation of test conditions, model size, number of lots, etc., is required)
Military specifications (tend to be extremely conservative)
Outside references (e.g., CS Electrical & Electronics, Jet Propulsion Lab for radiation data)

When executing a worst-case circuit performance research, the key aspects to be discussed within the system are dependent upon the conscious function of the circuit. Transfer functions of filtering networks, crucial timing of digital circuits, and characteristics of amplifiers are samples of circuit performance factors.

Worst case circuit analysis methodology

There are three popular methods used for worst-case circuit analysis, they are:

01. Monte Carlo Analysis (MCA)

Benefits

Delivers the most realistic assessment of the true worst-case implementation
Delivers additional details in the support of circuit or product risk estimation

Drawbacks

Needs use of a computer
Consumes a considerable amount of CPU time
Needs knowledge of part parameter pdf

02. Extreme Value Analysis (EVA)

Benefits

Most readily available estimation of worst-case performance (best initial WCCA method)
Does not need statistical inputs for circuit parameters (easiest to use)
The database demand only supplies part parameter variation extremes (easiest to apply)
If the circuit gives EVA, it will still function correctly (high confidence for necessary production applications)

Drawbacks

A pessimistic estimation of circuit worst-case performance
If the circuit falls, there is inadequate data to evaluate risk (modify the circuit to satisfy EVA needs, or use RSS or MCA for less conservativeness)

03. Root-SumSquared (RSS)

Benefits

More realistic estimation of worst-case implementation than EVA
Details of part parameter probability density function (pdf) are not needed
Delivers a limited degree of risk estimation(% of units to pass or fail)

Drawbacks

Standard deviation (σ) of piece part parameter probability distribution is needed
Guesses circuit sensitivities stay constant over a range of parameter variability
Guesses circuit performance variability obeys a normal distribution

Part statistics are founded on two kinds of component interpretation: random and bias. Random variation is not predictable in principle. Bias, nevertheless, is predictable given available inputs. All sources of element variation can be grouped into one of these outcomes. The outcomes are later combined to provide an overall representation of part variability. The expansion of individual incidental and biased variables is as follows:

Bias Effects: Added Algebraically
Random Effects: Root Sum Squared (±3σ boundaries of a normally distributed population)

Resolution of the minimum and highest limits of component importance due to drift is as follows:

WCCA, Worst Case Circuit Analysis formula

Example of Worst Case Circuit Analysis

The following illustration illustrates a usual calculation for selecting the worst-case minimum and maximum values for a 1200 microfarad CLR capacitor. These parameters are utilized to choose the potential resultant effect of CLR capacitor drifts on circuit applications.

Parameters: Capacitance	Bias (%) Negative	Bias (%) Positive	Random (%)
High Temp. (+80°C)	—	17	—
Low Temp. (-20°C)	28	—
Initial Tolerance at 25°C	—	—	20
Other-Envt’s (Hard Vacuum)	20	—	—
Radiation (10KR, 10^13 N/cm^2 )	—	12	—
Aging	—	—	10
TOTAL VARIATION	48	29	sqrt((20)^2 + (10)^2) = 22.4