Test-Path Scheduling for Interposer-Based 2.5D Integrated Circuits Using an Orthogonal Learning-Based Differential Evolution Algorithm

2.5D integrated circuits (ICs), which utilize an interposer to stack multiple dies side by side, represent a promising architecture for improving system performance, integration density, and design flexibility. However, the complex interconnect structures present significant challenges for post-fabrication testing, especially when scheduling test paths under constrained test access mechanisms. This paper addresses the test-path scheduling problem in interposer-based 2.5D ICs, aiming to minimize both total test time and cumulative inter-die interconnect length. We propose an efficient orthogonal learning-based differential evolution algorithm, named OLELS-DE. The algorithm combines the global optimization capability of differential evolution with an orthogonal learning-based search strategy and an elites local search strategy to enhance the convergence and solution quality. Comprehensive experiments are conducted on a set of benchmark instances with varying die counts, and the proposed method is compared against five state-of-the-art metaheuristic algorithms and CPLEX. Experimental results demonstrate that OLELS-DE consistently outperforms the competitors in terms of test cost reduction and convergence reliability, confirming its robustness and effectiveness for complex test scheduling in 2.5D ICs.