Gaps Emerge In Test And Analytics


Sensor and process drift, increased design complexity, and continued optimization of circuitry throughout its lifetime are driving test and analytics in new directions, requiring a series of base comparisons against which equipment and processes can be measured. In the design world this type of platform is called a digital twin, but in the test world there is no equivalent today. And as more... » read more

Testing In Context Gaining Ground


Testing in context is beginning to gain wider appeal as chip complexity increases, and as ICs are deployed in more safety-critical and mission-critical applications. While design in context has been the norm for SoCs for some time, a similar approach in test has been slow going. Cell-aware testing technology was first described a decade ago, and since then its adoption has been modest. But w... » read more

Using Machine Learning To Break Down Silos


Jeff David, vice president of AI solutions at PDF Solutions, talks with Semiconductor Engineering about where machine learning can be applied into semiconductor manufacturing, how it can be used to break down silos around different process steps, how active learning works with human input to tune algorithms, and why it’s important to be able to choose different different algorithms for differ... » read more

Different Ways To Improve Chip Reliability


A push toward greater reliability in safety- and mission-critical applications is prompting some innovative approaches in semiconductor design, manufacturing, and post-production analysis of chip behavior. While quality over time has come under intensive scrutiny in automotive, where German carmakers require chips to last 18 years with zero defects, it isn't the only market demanding extende... » 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

Week In Review: Manufacturing, Test


Chipmakers China has created a new $29 billion fund to help advance its semiconductor sector, according to reports from Bloomberg and others. Here's another report. The The U.S. and China are in the midst of a trade war. This has prompted China to accelerate its efforts to become more self-sufficient in semiconductor design and production. This includes DRAMs as well as logic/foundry. -----... » 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

Circuit-Device Co-design for High Performance Mixed-Signal Technologies


System-on-Chip designs require low cost integration of analog and digital blocks. Often, the analog requirements are not considered sufficiently early in the device design cycle, resulting in devices that are suboptimal for the analog components. This paper presents an innovative methodology for deriving comprehensive device specifications based upon a set of Figure-ofMerit circuits which accou... » read more

How Hardware Can Bias AI Data


Clean data is essential to good results in AI and machine learning, but data can become biased and less accurate at multiple stages in its lifetime—from moment it is generated all the way through to when it is processed—and it can happen in ways that are not always obvious and often difficult to discern. Blatant data corruption produces erroneous results that are relatively easy to ident... » read more

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