The increasing prevalence of counterfeit microelectronics in critical infrastructure, including defense, aerospace, and medical systems, presents severe risks to security, reliability, and operational efficiency. Counterfeit components can result in system malfunctions, breaches, and costly disruptions. Current detection methods, while varied, often lack the accuracy, repeatability, and ease of integration needed for broad industrial adoption. There is a pressing need for a robust, practical solution that can reliably authenticate microelectronic components across diverse applications. This proposal aims to develop an advanced counterfeit detection system that leverages Second Order Effect (SOE) signatures—distinctive patterns in the behavior of authentic devices under varied electromagnetic, thermal, and mechanical excitations. Our approach integrates multimodal SOE inspection, high-resolution imaging, digital twin modeling, and automated machine learning algorithms into a single, comprehensive detection platform. The system will use a combination of established and novel SOE techniques, enabling accurate, non-destructive authentication without requiring specialized device knowledge or golden samples. Through a phased development process, we will design, build, and validate this SOE-based platform, demonstrating its effectiveness in industry-relevant scenarios. This technology will provide end-users with an efficient tool to protect supply chains, enhance quality assurance, and prevent counterfeit components from compromising critical systems. Our solution represents a transformative advance in counterfeit detection, offering unparalleled accuracy, scalability, and ease of use to meet the growing demands of modern electronics markets.