P‐54: XAI Models for Efficient Analysis of Temperature and Power Consumption in High‐Brightness Panels and Modules

Abstract

Predicting temperature and power consumption in panels and modules under high brightness has long been a daunting and time‐intensive task, often requiring over a week of simulation work. Addressing this challenge, our study introduces a novel machine learning framework, bifurcated into panel and module stages, to streamline the data acquisition process. A key innovation in our approach is the use of symbolic regression to overcome the limitations posed by the reliance on real‐world measurements for power consumption and heat generation. Employing SPICE simulations, we estimated the current density within the panel, with subsequent validation through Explainable AI (XAI) analysis. This revealed a significant correlation between current density and panel heat generation. Our research also revisits the traditionally neglected areas outside the panel's center, uncovering their impact on heat generation. The layered complexity in modules, previously a barrier to physical sample creation and measurement, was navigated using simulation. XAI insights demonstrated the crucial roles of graphite and metal layers in heat dissipation, and the drive IC as a primary heat source. This integrated approach of real and computational data in machine learning significantly reduced the analysis timeframe from a week to mere minutes, marking a breakthrough in predictive accuracy and efficiency for high‐brightness scenarios.