Anne Meixner

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Anne Meixner is a contributing editor at Semiconductor Engineering. She has 30+ years in the semiconductor industry. She became fascinated by defects in the semiconductor manufacturing process as a young engineer at IBM. Over that period, she has focused on test methodology with emphasis on mixed-signal and analog DFT and test. As a technical communicator she takes complex ideas and explains them in consumable understandable pieces. Meixner has worked at IBM, Carnegie Mellon University and Intel. She holds three U.S. patents and her peers have recognized her work with two best papers at IEEE International Test Conference. She founded The Engineers’ Daughter in 2015 to consult on semiconductor testing and to coach engineers.

Author's Latest Posts

Why Silent Data Errors Are So Hard To Find
By Anne Meixner - 11 Oct, 2022 - Comments: 1

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
By Anne Meixner - 08 Sep, 2022 - Comments: 0

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
By Anne Meixner - 09 Aug, 2022 - Comments: 0

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
By Anne Meixner - 12 Jul, 2022 - Comments: 0

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
By Anne Meixner - 07 Jun, 2022 - Comments: 0

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
By Anne Meixner - 07 Jun, 2022 - Comments: 0

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
By Anne Meixner - 10 May, 2022 - Comments: 0

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

Finding And Applying Domain Expertise In IC Analytics
By Anne Meixner - 12 Apr, 2022 - Comments: 0

Behind PowerPoint slides depicting the data inputs and outputs of a data analytics platform belies the complexity, effort, and expertise that improve fab yield. With the tsunami of data collected for semiconductor devices, fabs need engineers with domain expertise to effectively manage the data and to correctly learn from the data. Naively analyzing a data set can lead to an uninteresting an... » read more

Auto Chipmakers Dig Down To 10ppb
By Anne Meixner - 08 Mar, 2022 - Comments: 0

How do engineers deliver 10 defective parts per billion (Dppb) to auto makers if they only screen 1 million parts per year? Answer: By comprehending failure mechanisms and proactively screening for them. Modern automobiles contain nearly 1,000 ICs that must perform over the vehicle’s life (15 years). This drives quality expectations ever higher. While 10 Dppm used to be a solid benchmark, ... » read more

Big Payback For Combining Different Types Of Fab Data
By Anne Meixner - 07 Dec, 2021 - Comments: 1

Collecting and combining diverse data types from different manufacturing processes can play a significant role in improving semiconductor yield, quality, and reliability, but making that happen requires integrating deep domain expertise from various different process steps and sifting through huge volumes of data scattered across a global supply chain. The semiconductor manufacturing IC data... » read more

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