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Nip The Defect In The Bud

Using an external inspection system and fault detection and classification software increases ABF substrate yield.

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As technology nodes shrink, end users are designing systems where each chip element is being targeted for a specific technology and manufacturing node. While designing chip functionality to address specific technology nodes optimizes a chip’s performance regarding that functionality, this performance comes at a cost: additional chips will need to be designed, developed, processed, and assembled to make a complete system solution.

At back-end packaging houses in the past, a multi-chip module (MCM) placed various packaged chips on a printed circuit board. Today in the advanced packaging space, fabless companies are using an Ajinomoto build-up film (ABF) substrate as a method of combining various chips into a smaller form factor. As the push for increased density in smaller multi-chip module packages increases, process cost increases as well. Along with rising costs, the cycle times needed to process ABF substrates with ever more redistribution layers (RDL) also increases. Consequently, the need for back-end packaging houses to maintain process control and detect defects is going to be similar to what front-end fabs encountered in the 1990s.

Breaking it down

Currently, substrates are 100µm to 150µm thick. As with front-end semiconductors, Moore’s Law is going to come into play with advanced substrate packaging technology. Line width/interconnects are going to shrink, and the need to be able to control and detect feedback will grow.

Reticle exposure on a non-ridged substrate inherently will require better control for rotational, scaling, orthogonal, and topology variation compensation. One solution is to use a feed-forward adaptive-shot technology to address process variations, die placement errors, and dimensionally unstable materials. Such a solution uses a parallel die-placement measurement process, while advanced analytics provide a means to balance productivity against yield.

Displacement errors can be measured on a lithography tool, but the measurements are slow, typically taking as much time to conduct as the exposure. But moving the measurements to a separate automated inspection system and feeding those corrections to the lithography system can double throughput. In addition, yield software adds predictive yield analysis to the externally conducted measurement and correction procedures and increases the number of die included in the exposure field up to a user-specified yield threshold.

Defect detection

The ability to detect defects throughout the substrate manufacturing process will grow as scaling shrinks and the complexity of MCM increases. Executing only a historic end-of-line, outgoing quality inspection will be insufficient for screening at-risk modules. As a result, using inline process monitoring to detect defects for high-end substrates will become mandatory.

Let’s consider the ABF substrate manufacturing process. If a particle comes into contact with the ABF substrate during the dry film lamination step, it may cause a RDL open defect during Cu plating. Since this defect will not be detected until the after-etch inspection step, the need to identify defects earlier in the process becomes a priority. If defects are detected early, rework or process mitigation can be conducted to determine the source of the defect and nip the defect in the bud.

Process tool control

As the manufacturing process matures, the need for tool-level fault detection control grows, and the ability to be alerted to defects or to shut down the tool is needed. The complexity and criticality of back-end packaging equipment will continue to increase, and the ability to have SEC/GEM-compliant protocol will be essential for proper factory automation and tracking.

With the current landscape and high-end substrate manufacturing process, a combination of software designed for fault detection and classification (FDC) controls will have to be implemented. Real-time FDC software detects abnormalities before they create scrap and helps identify faults before time is wasted finishing the process recipe. With real-time FDC software, faults are automatically characterized to reduce troubleshooting time. Meanwhile, run-time FDC software provides data insight and enables tool alarms to alert fab engineers to potential process drifts. Such an FDC platform provides real-time and run-time FDC solutions wherever process drift needs to be acted upon.

As the number of RDL grows and the overall processing cycle time increases, the feed-forward and feedback of process conditions during the formation of the MCM substrate will be required to maintain output. Stress or bow measurements on a substrate will be fed forward to advanced process control solutions, whereby lithography conditions are fed back to the run-to-run controller to compute conditions for feedback to the Cu deposition tool and feedforward to the stepper.

Conclusion

The high-end ABF substrate market will continue to grow and face many of the same difficulties that the front-end fabs faced in the past. With the current supply chain challenges, the need to maintain a constant supply of products becomes a priority, resulting in the need for better process control, better defect detection and better fault detection. By combining FDC software and a separate automated inspection system, back-end fabs can significantly increase yield.



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