Scan chain diagnosis is essential to solving yield-reduction problem caused by the miniaturization of manufacturing process. The accurate diagnosis of scan chain faults that frequently occur in the initial process is vital for rapidly improving yield. Moreover, the importance of scan chain diagnosis with a high resolution for the multiple faults is increasing because multiple faults occur in the early stages of the process, further increasing the cost of physical failure analysis. Although multiple faults can be diagnosed with existing methods, a high diagnostic resolution is difficult to achieve in the early stages of the process (where many faults occur) due to the rapid increase in the number of diagnosed fault candidates as the number of actual faults in the circuit increases. In this paper, a novel reconfigurable scan architecture that reconfigures the diagnosis paths and a test algorithm that uses this scan architecture are proposed to reduce the number of diagnosed fault candidates in the scan chain diagnosis with multiple circuit faults. Experimental results indicate that the proposed method achieves the higher diagnostic resolution for multiple faults than conventional methods. In addition, the proposed method reduces the routing overhead by scan partitioning.
Nanotechnology is an important technological alternative to overcome the limitations of complementary metal-oxide-semiconductor (CMOS) technology. Various circuit implementation methods based on nanotechnology have been proposed, and their most important characteristics are a high defect ratio and defect tolerance through circuit reconfiguration. CMOS-nanowire-MOLecular (CMOL) fieldprogrammable gate array (FPGA) circuits are advanced logic circuit structures that combine the advantages of CMOS and nanotechnology. However, researches on defect diagnosis methods for the reconfiguration of CMOL FPGA circuits are barely conducted. In this paper, efficient circuit configuration methods for defect diagnosis of the CMOL FPGA circuits are proposed to address the problem. Also, diagnosis algorithms for both stuck-at open and stuck-at close defects are introduced. Experimental results show that with the proposed methods, diagnosis is possible for CMOL fabrics with up to 20% stuck-at open defects and 0.02% or more stuck-at close defects.
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