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

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

Enablers And Barriers For Connecting Diverse Data
By Anne Meixner - 09 Nov, 2021 - Comments: 0

More data is being collected at every step of the manufacturing process, raising the possibility of combining data in new ways to solve engineering problems. But this is far from simple, and combining results is not always possible. The semiconductor industry’s thirst for data has created oceans of it from the manufacturing process. In addition, semiconductor designs large and small now ha... » read more

Coping With Parallel Test Site-to-Site Variation
By Anne Meixner - 09 Nov, 2021 - Comments: 1

Testing multiple devices in parallel using the same ATE results in reduced test time and lower costs, but it requires engineering finesse to make it so. Minimizing test measurement variation for each device under test (DUT) is a multi-physics problem, and it's one that is becoming more essential to resolve at each new process node and in multi-chip packages. It requires synchronization of el... » read more

One Test Is Not Always Enough
By Anne Meixner - 12 Oct, 2021 - Comments: 0

To improve yield, quality, and cost, two separate test parameters can be combined to determine if a part passes or fails. The results gleaned from that approach are more accurate, allowing test and quality engineers to fail parts sooner, detect more test escapes, and ultimately to improve yield and reduce manufacturing costs. New data analytic platforms, combined with better utilization of s... » read more

Fabs Drive Deeper Into Machine Learning
By Anne Meixner - 07 Sep, 2021 - Comments: 0

Advanced machine learning is beginning to make inroads into yield enhancement methodology as fabs and equipment makers seek to identify defectivity patterns in wafer images with greater accuracy and speed. Each month a wafer fabrication factory produces tens of millions of wafer-level images from inspection, metrology, and test. Engineers must analyze that data to improve yield and to reject... » read more

Why Wafer Bumps Are Suddenly So Important
By Anne Meixner - 10 Aug, 2021 - Comments: 2

Wafer bumps need to be uniform in height to facilitate subsequent manufacturing steps, but a push for 100% inspection in packaging in mission-critical markets is putting a strain on existing measurement technologies. Bump co-planarity is essentially a measure of flatness. Specifically, it measures the variation in bump height, which may have a target, for example, of about 100 microns. As a ... » read more

Geo-Spatial Outlier Detection
By Anne Meixner - 19 Jul, 2021 - Comments: 0

Comparing die test results with other die on a wafer helps identify outliers, but combining that data with the exact location of an outlier offers a much deeper understanding of what can go wrong and why. The main idea in outlier detection is to find something in or on a die that is different from all the other dies on a wafer. Doing this in the context of a die’s neighbor has become easie... » read more

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