How Overlay Keeps Pace With EUV Patterning


Overlay metrology tools improve accuracy while delivering acceptable throughput, addressing competing requirements in increasingly complex devices. In a race that never ends, on-product overlay tolerances for leading-edge devices are shrinking rapidly. They are in the single-digit nanometer range for the 3nm generation (22nm metal pitch) devices. New overlay targets, machine learning, and im... » read more

Using ML Methods In Production-Ready Engineering Solutions For IC Verification


By WeiLii Tan & Jeff Dyck Semiconductor designs continue to push the envelope of performance, functionality, and efficiency while their application scope expands in high-performance computing, automotive solutions, and IoT devices. The increased design complexity, scale, and mission-critical operations of semiconductor designs mean that IC verification strategies must evolve to cover expon... » read more

Analog Deep Learning Processor (MIT)


A team of researchers at MIT are working on hardware for artificial intelligence that offers faster computing with less power. The analog deep learning technique involves sending protons through solids at extremely fast speeds.  “The working mechanism of the device is electrochemical insertion of the smallest ion, the proton, into an insulating oxide to modulate its electronic conductivity... » read more

ML And UVM Share Same Flaws


A number of people must be scratching their heads over what UVM and machine learning (ML) have in common, such that they can be described as having the same flaws. In both cases, it is a flaw of omission in some sense. Let's start with ML, and in particular, object recognition. A decade ago, Alexnet, coupled with GPUs, managed to beat all of the object detection systems that relied on tradit... » read more

Publicly Available Dataset for PCB X-Ray Inspection (FICS- University of Florida)


Researchers from the Florida Institute for Cybersecurity (FICS) at the University of Florida published this technical paper titled "FICS PCB X-ray: A dataset for automated printed circuit board inter-layers inspection." Abstract "Advancements in computer vision and machine learning breakthroughs over the years have paved the way for automated X-ray inspection (AXI) of printed circuit bo... » read more

Using AI To Speed Up Edge Computing


AI is being designed into a growing number of chips and systems at the edge, where it is being used to speed up the processing of massive amounts of data, and to reduce power by partitioning and prioritization. That, in turn, allows systems to act upon that data more rapidly. Processing data at the edge rather than in the cloud provides a number of well-documented benefits. Because the physi... » read more

Machine Learning Application For Early Power Analysis Accuracy Improvement


In this paper, we introduce a machine learning (ML) application that accurately estimates the switching power of the cells without needing the SPEF file (SPEF less PA flow). Three ML models (multi-linear regression, random forest and decision tree) were trained and tested on different industrial designs at 7nm technology. They are trained using different cells’ properties available, SPEF, and... » read more

Effectiveness of a Reinforcement-Learning Based Dynamic Power Manager In a SW Framework


New technical paper titled "Low-Overhead Reinforcement Learning-Based Power Management Using 2QoSM" from researchers at ETH Zurich and Georgia Tech. Abstract "With the computational systems of even embedded devices becoming ever more powerful, there is a need for more effective and pro-active methods of dynamic power management. The work presented in this paper demonstrates the effectiven... » read more

Sibyl, a lightweight, reinforcement learning-based data placement technique for hybrid storage systems (ETH Zurich)


New research paper titled "Sibyl: Adaptive and Extensible Data Placement in Hybrid Storage Systems Using Online Reinforcement Learning" from researchers at ETH Zurich, Eindhoven University of Technology, and LIRMM, Univ. Montpellier, CNRS. Abstract "Hybrid storage systems (HSS) use multiple different storage devices to provide high and scalable storage capacity at high performance. Recent r... » read more

Coverage-Directed Test Selection Method for Automatic Test Biasing During Simulation-Based Verification


New research paper titled "Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification" from researchers at University of Bristol and Infineon Technologies. Abstract: "Constrained random test generation is one the most widely adopted methods for generating stimuli for simulation-based verification. Randomness leads to test diversity, but tests tend to repeate... » read more

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