Why Improving Auto Chip Reliability Is So Hard


Tools and ecosystems that focus on reliability and the long-term health of chips are starting to coalesce for the automotive electronics industry. Data gleaned from a chip’s lifecycle — design, verification, test, manufacturing, and in-field operation — will become key to achieving the longevity, reliability, functional safety, and security of newer generations of automobiles. Having s... » read more

A View Across The Siliconscape


What would it look like if you had the magical ability to look inside a chip and cast your eyes across the tumultuous activities within the silicon itself? If you could gaze into the die and see the real-time peaks and troughs of voltage supply, stressed areas with high activity and heat and areas of calm where uneven workloads create idle processor cores. A vision of the chip landscape, seasca... » read more

Achieving Physical Reliability Of Electronics With Digital Design


By John Parry and G.A. (Wendy) Luiten With today’s powerful computational resources, digital design is increasingly used earlier in the design cycle to predict zero-hour nominal performance and to assess reliability. The methodology presented in this article uses a combination of simulation and testing to assess design performance, providing more reliability and increased productivity. ... » read more

Improving Automotive Electronic Hardware With SAE J3168


By Theresa Duncan and Craig Hillman The race is on for fully autonomous vehicles. Industry giants like Tesla, Google, Uber and almost all major automotive companies are competing to deliver state-of-the-art self-driving vehicles. However, the development of new, cutting-edge technologies demands a similar wave of reliability, repairability and warranty standards that automotive manufactur... » read more

Why AI Systems Are So Hard To Predict


AI can do many things, but how to ensure that it does the right things is anything but clear. Much of this stems from the fact that AI/ML/DL systems are built to adapt and self-optimize. With properly adjusted weights, training algorithms can be used to make sure these systems don't stray too far from the starting point. But how to test for that, in the lab, the fab and in the field is far f... » read more

Using Analytics To Reduce Burn-in


Silicon providers are using adaptive test flows to reduce burn-in costs, one of the many approaches aimed at stemming cost increases at advanced nodes and in advanced packages. No one likes it when their cell phone fails within the first month of ownership. But the problems are much more pressing when the key components in data warehouse servers or automobiles fail. Reliability expectations ... » read more

Machine Learning Approach for Fast Electromigration Aware Aging Prediction in Incremental Design of Large Scale On-Chip Power Grid Network


Abstract "With the advancement of technology nodes, Electromigration (EM) signoff has become increasingly difficult, which requires a considerable amount of time for an incremental change in the power grid (PG) network design in a chip. The traditional Black’s empirical equation and Blech’s criterion are still used for EM assessment, which is a time-consuming process. In this article, for ... » read more

PowerPlanningDL: Reliability-Aware Framework for On-Chip Power Grid Design using Deep Learning


Academic research paper from Dept. of CSE, IIT Guwahatim, India. Abstract: "With the increase in the complexity of chip designs, VLSI physical design has become a time-consuming task, which is an iterative design process. Power planning is that part of the floorplanning in VLSI physical design where power grid networks are designed in order to provide adequate power to all the underlying ... » read more

Variation Threat In Advanced Nodes, Packages Grows


Variation is becoming a much bigger and more complex problem for chipmakers as they push to the next process nodes or into increasingly dense advanced packages, raising concerns about the functionality and reliability of individual devices, and even entire systems. In the past, almost all concerns about variation focused on the manufacturing process. What printed on a piece of silicon didn't... » read more

Revealing DRAM Operating GuardBands through Workload-Aware Error Predictive Modeling


Abstract Abstract—Improving the energy efficiency of DRAMs becomes very challenging due to the growing demand for storage capacity and failures induced by the manufacturing process. To protect against failures, vendors adopt conservative margins in the refresh period and supply voltage. Previously, it was shown that these margins are too pessimistic and will become impractical due to high ... » read more

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