• No or low throughput impact: It is essential that such a system minimally affects the test floor's throughput to an extent justifiable by the returns in the yield. This would involve ATE improvements.
• Scalable and efficient storage and compute infrastructure: Such storage and infrastructure enables logging and analyzing large amounts of data with extreme automation to handle multiple tools and flows.
• Accuracy: The diagnosis tools should be able to identify the failing cell, layer, bridges, and scan-chains and interconnect with high accuracy. ATE should allow accurate data capture as well.
• DFT support: The diagnosis tools should be able to diagnose failures using the outputs available from the compression structures placed on the chip. In addition, diagnosis tools should be interoperable over the DFT structures being placed by tools from other EDA vendors.
• Standard for data logging format: In the presence of multiple ATE and EDA vendors, a standard data-logging format is need to enable smooth information flow from ATEs to diagnosis tools.
• Correlation to other monitoring data: It is essential to correlate the volume diagnostics data with data obtained from other yield-monitored processes, such as in-line inspection, and provide necessary data-mining tools to create defect models and to handle the complex yield issues.