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Screening For Silent Data Errors


Engineers are beginning to understand the causes of silent data errors (SDEs) and the data center failures they cause, both of which can be reduced by increasing test coverage and boosting inspection on critical layers. Silent data errors are so named because if engineers don’t look for them, then they don’t know they exist. Unlike other kinds of faulty behaviors, these errors also can c... » read more

Adopting Predictive Maintenance On Fab Tools


Predictive maintenance, based on more and better sensor data from semiconductor manufacturing equipment, can reduce downtime in the fab and ultimately cut costs compared with regularly scheduled maintenance. But implementing this approach is non-trivial, and it can be disruptive to well-honed processes and flows. Not performing maintenance quickly enough can result in damage to wafers or the... » read more

Test Connections Clean Up With Real-Time Maintenance


Test facilities are beginning to implement real-time maintenance, rather than scheduled maintenance, to reduce manufacturing costs and boost product yield. Adaptive cleaning of probe needles and test sockets can extend equipment lifetimes and reduce yield excursions. The same is true for load board repair, which is moving toward predictive maintenance. But this change is much more complicate... » read more

Why Silent Data Errors Are So Hard To Find


Cloud service providers have traced the source of silent data errors to defects in CPUs — as many as 1,000 parts per million — which produce faulty results only occasionally and under certain micro-architectural conditions. That makes them extremely hard to find. Silent data errors (SDEs) are random defects produced in manufacturing, not a design bug or software error. Those defects gene... » read more

The Drive Toward More Predictive Maintenance


Maintenance is a critical behind-the-scenes activity that keeps manufacturing facilities running and data centers humming. But when not performed in a timely manner, it can result in damaged products or equipment, or significant system/equipment downtime. By shifting from scheduled maintenance to predictive maintenance, factories and electronic system owners can reap substantial benefits, in... » read more

Making The Most Of Data Lakes


Having all the semiconductor data available is increasingly necessary for improving manufacturability, yield, and ultimately the reliability of end devices. But without sufficient knowledge of relationships between data from different processes and computationally efficient data structures, the value of any data is significantly reduced. In the semiconductor industry, reducing waste, decreas... » read more

Finding Frameworks For End-To-End Analytics


End-to-end analytics can improve yield and ROI on tool purchases, but reaping those benefits will require common data formats, die traceability, an appropriate level of data granularity — and a determination of who owns what data. New standards, guidelines, and consortium efforts are being developed to remove these barriers to data sharing for analytics purposes. But the amount of work req... » read more

Removing Barriers For End-To-End Analytics


Parties are coming together, generating guidelines for sharing data from IC design and manufacturing through end of life, setting the stage for true end-to-end analytics. While the promise of big data analytics is well understood, data sharing through the semiconductor supply chain has been stymied by an inability to link together data sources throughout the lifecycle of a chip, package, or ... » read more

The Race To Zero Defects In Auto ICs


Assembly houses are fine-tuning their methodologies and processes for automotive ICs, optimizing everything from inspection and metrology to data management in order to prevent escapes and reduce the number of costly returns. Today, assembly defects account for between 12% and 15% of semiconductor customer returns in the automotive chip market. As component counts in vehicles climb from the ... » read more

Where And When End-to-End Analytics Works


With data exploding across all manufacturing steps, the promise of leveraging it from fab to field is beginning to pay off. Engineers are beginning to connect device data across manufacturing and test steps, making it possible to more easily achieve yield and quality goals at lower cost. The key is knowing which process knob will increase yield, which failures can be detected earlier, and wh... » read more

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