Fault diagnosis and verification play a crucial role in ensuring the reliability and correctness of Very Large Scale Integration (VLSI) circuits, especially as modern digital systems continue to grow in complexity and scale. With the continuous reduction in transistor sizes and increasing integration density, digital circuits are more susceptible to logic- level faults arising from design inconsistencies, manufacturing variations, timing violations, and unexpected input conditions. Early identification of such faults is essential to prevent functional failures, reduce debugging time, and improve overall system reliability. Traditional fault detection techniques predominantly rely on rule-based testing, manual inspection, or hardware-intensive verification tools. While these methods have been effective for small and medium-scale circuits, they often become inefficient, time- consuming, and difficult to scale when applied to complex digital systems. Additionally, many conventional approaches focus solely on fault identification without offering meaningful guidance for fault correction, which limits their usefulness in educational and early-stage design environments. To address these limitations, this paper presents an AI-inspired fault detection framework for digital VLSI circuits based on logic gate simulation and intelligent decision analysis. Rather than employing data-intensive machine learning models, the proposed system emphasizes deterministic logic evaluation, transparency, and interpretability. Fundamental digital logic gates are modeled using Boolean logic principles, and the system evaluates circuit behavior by comparing observed outputs with theoretically expected results derived from standard truth tables. The proposed framework not only detects faulty behavior at the logic level but also provides corrective insights by analyzing input-output relationships. When a mismatch between expected and observed outputs is identified, the system examines possible input variations and logical conditions that may have caused the fault. This feature enables users to better understand the nature of the fault and assists designers in rapid debugging and validation of digital circuits. A complete software-based implementation of the proposed system is developed using Python for backend logic processing, while the Flask framework is utilized to create a web-based interactive user interface. The frontend allows users to select logic gate types, provide input combinations, and specify observed outputs, making the system accessible even to users with limited hardware design experience. This modular and platform- independent implementation ensures ease of deployment, low cost, and scalability for academic and experimental use. Experimental evaluation demonstrates that the proposed approach accurately detects logic-level faults across a wide range of fundamental logic gates, including AND, OR, NOT, NAND, NOR, XOR, and XNOR configurations. The results indicate consistent and reliable fault identification while maintaining low computational overhead. Compared to conventional fault detection methodologies, the proposed system offers improved interpretability, reduced complexity, and enhanced user interaction. Overall, the presented AI-inspired fault detection framework provides a cost-effective, user-friendly, and educationally valuable solution for logic-level fault analysis in VLSI circuits. The system is particularly suitable for academic learning, design verification, and preliminary testing scenarios, and it serves as a strong foundation for future extensions involving sequential circuits, larger combinational networks, and advanced intelligent diagnostic techniques.
VLSI fault detection, logic gate simulation, artificial intelligence, machine learning-inspired systems, digital circuits, fault diagnosis.
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