Electrical Engg - The Performance of Electronic Circuits.

Electrical Engg.
EEE 119

Abstract: -
In this report, we have to demonstrate the system engineering by modelling and analysis that
evaluate the performance of electronic circuits. The various analysis techniques can analyze
tolerance capacity of the individual component. The main aim of this study is to plot the different
function for circuit analyses.
1.Introduction:-
Analysis techniques are used to calculate the performance of the electronic circuits. MATLAB
tool is used to analyze the tolerance of the component. Evaluation of tolerance sensitivity is
essential because it impacts the yield, which is used to predict the success rate of the circuit
design and making the system or the product [1].
Yield = Number of products meeting to specification/ Total number of manufactured product
For designing a system, it is essential to maximize the yield so that wastage should be minimum
and profit can be maximized.
2.Methodology:-
2.1Analysis Techniques:-
Analysis techniques in a given circuit analyze the component tolerance effect. It is basically of
two type name as EVA (Extreme Value Analysis): it evaluates the extreme and nominal values
of the circuit for component tolerance or individual component. EVA is a tool that provides the
extraction facility for intense value series for observation records. From this, we can understand
the effect of the component on the circuit performance [3].
RT (1±ΔRT) = R1 (1±ΔR) + R2 (1±ΔR)………………..(1)
Above equation showing the combination of resistor component and ΔR represents the deviation
in the tolerance capacity of the resistance.
Other is Monte Carlo Analysis is used for simulation of the circuit with probability density
function. It is a statistical model to analyze the behaviour of the circuit. With the help of a

computer program, a large number of component values can be generated within the tolerance so
that simulation of individual value can predict the behaviour of the circuit [4].
Plotting of function shows the show's relation between the variables. From this, we can analyze
the maximum and minimum value of the function. Resulting output sample from the Monte
Carlo simulation can be recorded. It provides a comprehensive view of what may happen, it not
only provides the information what could happen, also tells how likely it is to happen. It gives
the result of different outcomes and chances of occurrence in the graphical format, which is easy
to understand [2]. This analytic technique provides the correlation with the input. It is also
important to represent accuracy when some factors go up or down.
3.Results:-
Graphical Representation of Signals:-
Figure 1:- Shows the Sine wave of 1Hz signal
The graph shows the variation in amplitude with respect to time. At low-frequency amplitude is
very high reach to the value 10.

Figure 2:- Shows the Cos wave of 2 Hz signal
The Graph Cosine wave shows the variation in amplitude with respect to time. At high-
frequency amplitude goes on decreases.
Figure 3:-Represents the waveform of sine and cosine function
From the above graph, we can analyze the variation in the 1Hz sine wave and 2Hz sine wave.
CorrelATION between two parameters can be analyzed with the input. Frequency of wave is
changing with the amplitude. On increases, the frequency amplitude goes on decreases. This

graph shows the correlation between the several parameters to input and output. We the help of
this analysis, we can predict the circuit component during the designing process.
Conclusion:-
In this study, we plot various function and analyze the behaviour of the parameters. So, we
predict the multiple factors such as ambiguity, variability and uncertainty.

References:-
[1]F. Bonato, A. Albuquerque and M. Paixão, "An application of Earned Value Management
(EVM) with Monte Carlo simulation in engineering project management", Gestão & Produção,
vol. 26, no. 3, 2019. Available: 10.1590/0104-530x4641-19.
[2]"What is Monte Carlo Simulation?", Monte Carlo Simulation: What Is It and How Does It
Work? - Palisade, 2020. [Online]. Available:
https://www.palisade.com/risk/monte_carlo_simulation.asp. [Accessed: 11- Apr- 2020].
[4]L. Mentaschi et al., "The transformed-stationary approach: a generic and simplified
methodology for non-stationary extreme value analysis", Hydrology and Earth System Sciences,
vol. 20, no. 9, pp. 3527-3547, 2016. Available: 10.5194/hess-20-3527-2016.
[3]“Digital Systems
Engineering,”file:///C:/Users/pc-le040/AppData/Local/Temp/824446611_Lecture9-
Assignment2020-Slides-3.pdf