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

Achieving Zero Defect Manufacturing Part 1: Detect & Classify


Whether the discussion is about smart manufacturing or digital transformation, one of the biggest conversations in the semiconductor industry today centers on the tremendous amount of data fabs collect and how they utilize that data. While chip makers are accumulating petabytes of data across the entire semiconductor process, a question arises: how much of that information is being fully uti... » 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

The Future Of Data Analytics And Semiconductor Testing


The world is changing more rapidly than ever. With the explosion of Artificial Intelligence (AI), Machine Learning (ML) and data analytics, semiconductor manufacturers now have the opportunity to extract valuable insights from the massive amounts of data being generated throughout the silicon lifecycle. By leveraging AI algorithms and ML, semiconductor manufacturers can now optimize silicon des... » read more

Chip Design Digs Deeper Into AI


Growing demand for blazing fast and extremely dense multi-chiplet systems are pushing chip design deeper into AI, which increasingly is viewed as the best solution for sifting through scores of possible configurations, constraints, and variables in the least amount of time. This shift has broad implications for the future of chip design. In the past, collaborations typically involved the chi... » read more

AI-Powered Data Analytics To Revolutionize The Semiconductor Industry


In the age where data reigns supreme, the semiconductor industry stands on the cusp of revolutionary change, redefining complexity and productivity through a lens crafted by artificial intelligence (AI). The intersection of AI and the semiconductor industry is not merely an emerging trend—it is the fulcrum upon which the next generation of technological innovation balances. Semiconductor comp... » read more

AI For Data Management


Data management is becoming a significant new challenge for the chip industry, as well as a brand new opportunity, as the amount of data collected at every step of design through manufacturing continues to grow. Exacerbating the problem is the rising complexity of designs, many of which are highly customized and domain-specific at the leading edge, as well as increasing demands for reliabili... » read more

EDA Looks Beyond Chips


Large EDA companies are looking at huge new opportunities that reach well beyond semiconductors, combining large-scale multi-physics simulations with methodologies and tools that were developed for chips. Top EDA executives have been talking about expanding into adjacent markets for more than a decade, but the broader markets were largely closed to them. In fact, the only significant step in... » read more

Increased Automotive Data Use Raises Privacy, Security Concerns


The amount of data being collected, processed, and stored in vehicles is exploding, and so is the value of that data. That raises questions that are still not fully answered about how that data will be used, by whom, and how it will be secured. Automakers are competing based on the latest versions of advanced technologies such as ADAS, 5G, and V2X, but the ECUs, software-defined vehicles, an... » read more

Utilizing Artificial Intelligence For Efficient Semiconductor Manufacturing


The challenges before semiconductor fabs are expansive and evolving. As the size of chips shrinks from nanometers to eventually angstroms, the complexity of the manufacturing process increases in response. It can take hundreds of process steps and more than a month to process a single wafer. It can subsequently take more than another month to go through the assembly, testing, and packaging st... » read more

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