Achieving Zero Defect Manufacturing Part 3: Prevention Of Defects


The concept of zero defect manufacturing has been around for decades, arising first in the aerospace and defense industry. Since then, this manufacturing approach has been adopted by the automotive industry, and it has only grown in importance as the sector transitions to electric vehicles. Given the role semiconductors play in today’s vehicles, and will play in the future, it is no surprise ... » read more

Challenges And Outlook Of ATE Testing For 2nm SoCs


The transition to the 2nm technology node introduces unprecedented challenges in Automated Test Equipment (ATE) bring-up and manufacturability. As semiconductor devices scale down, the complexity of testing and ensuring manufacturability increases exponentially. 3nm silicon is a mastered art now, with yields hitting pretty high for even complex packaged silicon, while the transition from 3nm to... » read more

Achieving Zero Defect Manufacturing Part 2: Finding Defect Sources


Semiconductor manufacturing creates a wealth of data – from materials, products, factory subsystems and equipment. But how do we best utilize that information to optimize processes and reach the goal of zero defect manufacturing? This is a topic we first explored in our previous blog, “Achieving Zero Defect Manufacturing Part 1: Detect & Classify.” In it, we examined real-time defe... » read more

Using Predictive Data Analytics In Manufacturing


Data is said to be the gold of the 21st century, but is that true? Even with trillions of lines of data in your database, you won’t be mining any gold – unless you understand what the data means. Here’s what’s happening all around the semiconductor industry: we have far too much data. The problem is that the value you need is hidden in the data, and to mine the gold from it, you need to... » read more

Memory On Logic: The Good And Bad


The chip industry is progressing rapidly toward 3D-ICs, but a simpler step has been shown to provide gains equivalent to a whole node advancement — extracting distributed memories and placing them on top of logic. Memory on logic significantly reduces the distance between logic and directly associated memory. This can increase performance by 22% and reduce power by 36%, according to one re... » read more

DTCO/STCO Create Path For Faster Yield Ramps


Higher density in planar SoCs and advanced packages, coupled with more complex interactions and dependencies between various components, are permitting systematic defects to escape traditional detection methods. These issues increasingly are not detected until the chips reach high-volume manufacturing, slowing the yield ramp and bumping up costs. To combat these problems, IDMs and systems co... » read more

Partnership To Improve Semiconductor Quality And Yield


By Eran Rousseau (NI) and Eli Roth (Teradyne) The semiconductor industry is notorious for its high production costs and the critical importance of maintaining impeccable product quality. As technology advances and consumer expectations rise, semiconductor companies face constant pressure to meet these cost and quality goals while also delivering cutting-edge products. Traditionally, the s... » read more

Yield Tracking In RDL


Yield is a much bigger issue when it comes to panel-level packages, which may contain up to 24 RDL layers. Just finding the defects is a massive challenge, let alone understanding how they will impact the entire device. Many of these advanced packages are being used in data centers for generative AI, and killer defects caused by bridges and opens can cause serious problems. What happens, for in... » read more

ML-Assisted IC Test Binning With Real-Time Prediction At The Edge


IC Test is a critical part of semiconductor manufacturing and proper die binning and material disposition has an important impact on the overall yield and on the process monitoring and failure mode diagnostics. Edge analytics are becoming an increasingly important aspect of die disposition. By intercepting parts in real-time at the wafer test step, we can save downstream processing needs. In th... » read more

Fabs Begin Ramping Up Machine Learning


Fabs are beginning to deploy machine learning models to drill deep into complex processes, leveraging both vast compute power and significant advances in ML. All of this is necessary as dimensions shrink and complexity increases with new materials and structures, processes, and packaging options, and as demand for reliability increases. Building robust models requires training the algorithms... » read more

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