5 Steps To Becoming A Data-Driven Manufacturer


What does it take to become a data-driven manufacturer. There are five fundamental steps: Get the data you need Organize the data Analyze and present the data Automate analytics Innovate Companies constantly strive to become data-driven. In this way, their decisions can become more objective and more likely to achieve the desired results. In fact, many companies understand t... » read more

Building Powertrains To Meet EV Demand


Throughout history, customer demand has always been a driving factor in companies’ product portfolios around the world. Today, the demand for a cleaner earth is unparalleled, with individuals wanting products and services that minimize harm to the environment. This amazing transition in customer demand is apparent everywhere. The food industry is experiencing a rise in vegan requests, the ... » read more

Leveraging Data In Chipmaking


John Kibarian, president and CEO of PDF Solutions, sat down with Semiconductor Engineering to talk about the impact of data analytics on everything from yield and reliability to the inner structure of organizations, how the cloud and edge will work together, and where the big threats are in the future. SE: When did you recognize that data would be so critical to hardware design and manufact... » read more

October ’19 Startup Funding: Mega Harvest


Seventeen startups took in mega-rounds of $100 million or more during October, with a cumulative total of just over $3.2 billion. Cybersecurity startups continued to be popular with private investors during the month of October, with 15 financing rounds. Twenty automotive and mobility technology firms picked up new investments. Analytics firms, artificial intelligence/machine learning techno... » read more

Making Random Variation Less Random


The economics for random variation are changing, particularly at advanced nodes and in complex packaging schemes. Random variation always will exist in semiconductor manufacturing processes, but much of what is called random has a traceable root cause. The reason it is classified as random is that it is expensive to track down all of the various quirks in a complex manufacturing process or i... » read more

Reducing Costly Flaws In Heterogeneous Designs


The cost of defects is rising as chipmakers begin adding multiple chips into a package, or multiple processor cores and memories on the same die. Put simply, one bad wire can spoil an entire system. Two main issues need to be solved to reduce the number of defects. The first is identifying the actual defect, which becomes more difficult as chips grow larger and more complex, and whenever chi... » read more

From Womb To Tomb: A Lifetime Of Chip Data In A Common Language


Every integrated circuit (IC) has a lifetime of stories to tell. From design through the end of a chip’s life, it can let us know what’s happening all along the way, providing we give it a voice and the language to do so. But until we can gain access to this data, the lives of these ICs remain secret. In-chip monitoring opens up those secrets. It helps to optimize performance, and it is esp... » read more

Testing Against Changing Standards In Automotive


The infusion of more semiconductor content into cars is raising the bar on reliability and changing the way chips are designed, verified and tested, but it also is raising a lot of questions about whether companies are on the right track at any point in time. Concerns about liability are rampant with autonomous and assisted driving, so standards are being rolled out well in advance of the te... » read more

Machine Learning For ADAS Camera Manufacturing


Virtually every vehicle manufacturer in the world is either developing, purchasing, or investing in ADAS systems in order to bring autonomous vehicles into the market. With this demand on the rise, the need for high quality automotive camera modules is rising. ADAS systems are built using computer vision technology and act as the “eyes” of autonomous vehicles. Numerous cameras are embedd... » read more

Using Better Data To Shorten Test Time


The combination of machine learning plus more sensors embedded into IC manufacturing equipment is creating new possibilities for more targeted testing and faster throughput for fabs and OSATs. The goal is to improve quality and reduce the cost of manufacturing complex chips, where time spent in manufacturing is ballooning at the most advanced nodes. As the number of transistors on a die incr... » read more

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