We present a comprehensive framework for p–n junction simulation that bridges analytical models, numerical methods, and machine learning approaches. A hybrid finite-difference physics-informed neural network solver is developed and validated across 12 silicon devices with doping concentrations ranging from \(10^{15}\) to \(10^{19}\,\textrm{cm}^{-3}\) . The surrogate model reproduces Sentaurus TCAD results with \(0.48\%\) RMS potential error while revealing that analytical models systematically underestimate the built-in potential by up to 8 mV at high doping concentrations. This error leads to a \(3\%\) overestimation of solar cell short-circuit current in predictive models. The framework achieves a \(47\times\) speed-up compared to conventional TCAD simulations and is successfully extended to 2D geometries ( \(50 \times 50\) mesh) without architectural modifications, maintaining 0.62% RMS error with \(12\times\) acceleration. All implementation code, datasets, and reproduction scripts are openly available under an MIT license.
