Chip Industry’s Technical Paper Roundup: Nov. 29


New technical papers added to Semiconductor Engineering’s library this week. [table id=66 /]   Related Reading: Chip Industry’s Technical Paper Roundup: Nov. 21 New papers: lithography modeling; solving Rowhammer; energy-efficient batch normalization HW; 3-to-1 reconfigurable analog signal modulation circuit; lateral double magnetic tunnel junction; reduce branch mispredic... » read more

Phononic and Magnonic Properties of 1D MoI3 Nanowires


A new technical paper titled "Elemental excitations in MoI3 one-dimensional van der Waals nanowires" was published by researchers at NIST, UC Riverside, University of Georgia, Theiss Research Inc, and Stanford University. "We described here the elemental excitations in crystals of MoI3 a vdW [van der Waals] material with a true-1D crystal structure. Our measurements reveal anomalous temperat... » read more

Opportunities and Challenges for Carbon Nanotube Transistors


A new technical review paper titled "Carbon nanotube transistors: Making electronics from molecules" was published by researchers at Duke University, Northwestern University, and Stanford University. “Between the opportunities in high-performance digital logic with the potential for 3D integration and the possibilities for printed and even recyclable thin-film electronics, CNT transistors ... » read more

New Material for Printing At the Nanoscale, Strong & Light (Stanford/Northwestern)


A new technical paper titled "Mechanical nanolattices printed using nanocluster-based photoresists" was published by researchers at Stanford University and Northwestern University. The researchers have developed a new material for nanoscale 3D printing to be used for drones, microelectronics and satellites, demonstrating that "the new material is able to absorb twice as much energy than othe... » read more

Research Bits: Nov. 21


Graphene heater for phase-change switches Researchers from the University of Washington, Stanford University, Charles Stark Draper Laboratory, University of Maryland, and Massachusetts Institute of Technology designed an energy-efficient, silicon-based non-volatile switch that manipulates light through the use of a phase-change material and graphene heater. Aiming to reduce the power consum... » read more

Energy of Computing As A Key Design Aspect (SLAC/Stanford, MIT)


A technical paper titled "Trends in Energy Estimates for Computing in AI/Machine Learning Accelerators, Supercomputers, and Compute-Intensive Applications" was published by researchers at SLAC/Stanford University and MIT. Abstract: "We examine the computational energy requirements of different systems driven by the geometrical scaling law, and increasing use of Artificial Intelligence or Ma... » read more

Research Bits: Oct. 18


Modular AI chip Engineers at the Massachusetts Institute of Technology (MIT), Harvard University, Stanford University, Lawrence Berkeley National Laboratory, Korea Institute of Science and Technology, and Tsinghua University created a modular approach to building stackable, reconfigurable AI chips. The design comprises alternating layers of sensing and processing elements, along with LEDs t... » read more

Can ML Help Verification? Maybe


Functional verification produces an enormous amount of data that could be used to train a machine learning system, but it's not always clear which data is useful or whether it can help. The challenge with ML is understanding when and where to use it, and how to integrate it with other tools and approaches. With a big enough hammer, it is tempting to call everything a nail, and just throwing ... » read more

Technical Paper Roundup: Aug. 30


New technical papers added to Semiconductor Engineering’s library this week. [table id=47 /] Semiconductor Engineering is in the process of building this library of research papers. Please send suggestions (via comments section below) for what else you’d like us to incorporate. If you have research papers you are trying to promote, we will review them to see if they are a good fit for... » read more

ML-Based Framework for Automatically Generating Hardware Trojan Benchmarks


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

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