tag: machine learning

Recipe To Catch Bugs Faster Using Machine Learning
By Anika Sunda - 19 Sep, 2022 - Comments: 0

We all agree that verification and debug take up a significant amount of time and are arguably the most challenging parts of chip development. Simulator performance has consistently topped the charts and is a critical component in the verification process. Still, the need of the hour is to stretch beyond simulator speed to achieve maximum verification throughput and efficiency. Artificial in... » read more

Rethinking Machine Learning For Power
By Brian Bailey - 13 Sep, 2022 - Comments: 0

The power consumed by machine learning is exploding, and while advances are being made in reducing the power consumed by them, model sizes and training sets are increasing even faster. Even with the introduction of fabrication technology advances, specialized architectures, and the application of optimization techniques, the trend is disturbing. Couple that with the explosion in edge devices... » read more

New Data Management Challenges
By Ann Mutschler - 13 Sep, 2022 - Comments: 0

An explosion in semiconductor design and manufacturing data, and the expanding use of chips in safety-critical and mission-critical applications, is prompting chipmakers to collect and manage that data more effectively in order to improve overall performance and reliability. This collection of data reveals a number of challenges with no simple solutions. Data may be siloed and inconsistent, ... » read more

Modeling Effects Of Fluctuation Sources On Electrical Characteristics Of GAA Si NS MOSFETs Using ANN-Based ML
By Technical Paper Link - 07 Sep, 2022 - Comments: 0

Researchers from National Yang Ming Chiao Tung University (Taiwan) published a technical paper titled "A Machine Learning Approach to Modeling Intrinsic Parameter Fluctuation of Gate-All-Around Si Nanosheet MOSFETs." "This study has comprehensively analyzed the potential of the ANN-based ML strategy in modeling the effect of fluctuation sources on electrical characteristics of GAA Si NS MOSF... » read more

Deep Learning To Classify And Establish Structure Property Predictions With PeakForce QNM Atomic Force Microscopy
By Bruker - 07 Sep, 2022 - Comments: 0

Machine learning and specifically, deep learning, is a powerful tool to establish the presence (or absence) of microstructure correlations to bulk properties with its ability to flesh out relationships and trends that are difficult to establish otherwise. This application note discusses the use of deep learning tools, to explore AFM phase and PeakForce Quantitative Nanomechanics (QNM) im... » read more

Methods To Overcome Limited Labeled Data Sets In Machine Learning-Based Optical Critical Dimension Metrology
By Onto Innovation - 07 Sep, 2022 - Comments: 0

With the aggressive scaling of semiconductor devices, the increasing complexity of device structure coupled with tighter metrology error budget has driven up Optical Critical Dimension (OCD) time to solution to a critical point. Machine Learning (ML), thanks to its extremely fast turnaround, has been successfully applied in OCD metrology as an alternative solution to the conventional physical... » read more

Artificial Neural Network (ANN)-Based Model To Evaluate The Characteristics of A Nanosheet FET (NSFET)
By Technical Paper Link - 02 Sep, 2022 - Comments: 0

This new technical paper titled "Machine-Learning-Based Compact Modeling for Sub-3-nm-Node Emerging Transistors" was published by researchers at SungKyunKwan University, Korea. Abstract: "In this paper, we present an artificial neural network (ANN)-based compact model to evaluate the characteristics of a nanosheet field-effect transistor (NSFET), which has been highlighted as a next-generat... » read more

ML Architecture for Solving the Inverse Problem for Matter Wave Lithography: LACENET
By Technical Paper Link - 29 Aug, 2022 - Comments: 0

This recent technical paper titled "Realistic mask generation for matter-wave lithography via machine learning" was published by researchers at University of Bergen (Norway). Abstract: "Fast production of large area patterns with nanometre resolution is crucial for the established semiconductor industry and for enabling industrial-scale production of next-generation quantum devices. Metasta... » read more

ML-Based Framework for Automatically Generating Hardware Trojan Benchmarks
By Technical Paper Link - 25 Aug, 2022 - Comments: 0

A new technical paper titled "Automatic Hardware Trojan Insertion using Machine Learning" was published by researchers at University of Florida and Stanford University. Abstract (partial): "In this paper, we present MIMIC, a novel AI-guided framework for automatic Trojan insertion, which can create a large population of valid Trojans for a given design by mimicking the properties of a small... » read more

Machine Learning-Driven Full-Flow Chip Design Automation
By Cadence - 24 Aug, 2022 - Comments: 0

To enable the semiconductor industry to continue growing, the chip design process must become more efficient. With the availability of massive, cloud-enabled, distributed computing and advancements in machine learning computer science, the next chip design automation revolution is now possible. The Cadence® Cerebrus™ Intelligent Chip Explorer utilizes both of these technologies, based o... » read more

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