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Automated Optical Inspection


Building good automated models for inspection require more data to be collected, both good and bad. Vijay Thangamariappan, R&D engineer at Advantest, explains how to develop models for automating optical inspection, using a multi-thousand pin socket as an example for how machine learning has helped reduce the return rate due to defects from 2% down to zero. He also explains how to achieve t... » 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

Finding And Applying Domain Expertise In IC Analytics


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


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


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

End-To-End Traceability


Despite standards such as ISO 26262 and IEC 61508, there are still disconnects and gaps in the supply chain and design-through-manufacturing flows. Kurt Shuler, vice president of marketing at Arteris IP, digs into what's missing, why changes made in one area are not reflected in other areas and throughout the product lifecycle, and why various different phases of the flow don't always match up ... » read more

Enablers And Barriers For Connecting Diverse Data


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


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


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

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