<![CDATA[This paper introduces a deep reinforcement learning approach for optimizing network-on-chip layout verification and route optimization. The proposed method addresses the challenges of increasing design complexity and traditional verification limitations in modern VLSI circuits. A novel three-headed policy gradient network architecture is developed to handle layout verification and routing optimization tasks simultaneously. The framework integrates feature extraction networks for topology analysis, policy networks for decision-making, and value networks for performance evaluation. The system employs a complex-valued reinforcement learning model to capture both spatial and temporal dependencies in NoC designs. Experimental results demonstrate significant improvements across multiple performance metrics: a 23.4% reduction in average packet latency, a 23.6% increase in network throughput, and a 20.8% decrease in power consumption compared to conventional methods. The verification accuracy achieves 98.7% with a false positive rate below 0.5%. The framework maintains consistent performance across various network sizes and traffic patterns, demonstrating robust scalability and practical applicability in real-world chip designs. Implementation results on ISPD 2005 benchmarks validate the effectiveness of the proposed approach, showing superior performance in both verification accuracy and optimization efficiency compared to existing state-of-the-art methods.]]>
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