Comparative Analysis of Surface Electrical Potential Distribution Across Semiconductor Wafers Using Advanced 3D Simulation Models

Correct mapping of electrical potential using semiconductor wafer is critical to the operation, productivity, and homogeneity of gadgets particularly in the present-day trend in electronics and nearing the nanoscale. Traditional numerical and analysis methods, including Finite Differentiation, Finite Element, Drift Differences, and Poisson Boltzmann algorithms, may be challenging to resolve the fine details around the edges of wafer or high gradients, in contrast to data-driven algorithms, e.g. Neural Network Regression, which are fast without physical insight. To overcome all these shortcomings, this paper suggests a Hybrid Quantum Numerical Model (HQNM), which applies classical numerical computation together with terms of quantum correction to make it more accurate and physical. A set of six surface potential simulations of this model was compared to five known methods with the same boundary and material conditions. Findings indicate that HQNM has the best root mean square error, smoothness factor and correct edge performance at moderate cost of computation. The results demonstrate that the model is a promising approach to high-fidelity wafer-level potential mapping, a balanced solution to offer numerically stable, accurate, and computationally-efficient solutions.