The electrical components (such as resistors and capacitors) are mass-produced. Variations in raw materials and the manufacturing process mean that the component parameters have statistical distributions.

Considering the number of components in a circuit and the distribution of their parameters, the circuit's performance may not meet the specification requirements. This is a risk that must be identified, managed, and mitigated early in the design process.

Root Sum Square Analysis (RSSA) is a method that, under the condition that all input parameters are independent and the mean and variance of each parameter's distribution are known, considers the sensitivity of performance to each parameter. The standard deviation of the circuit's performance is calculated as the square root of the sum of the squares of the standard deviations. Typically, when the sample size is large, the circuit's performance can be assumed to follow a normal distribution, allowing the determination of extreme values for the performance parameters at a given probability (e.g., 2σ for 95.44%).

This application uses the RSSA method to analyze the performance of the circuit. Compared to the Monte Carlo method, the square root analysis method has the advantages of smaller computational effort and higher accuracy when the component parameters are independent and follow a normal distribution. However, if the parameters are known to follow a non-normal distribution, the Monte Carlo method is more effective for calculations.