tag: IBM Research

Plasma Etching : Challenges And Options Going Forward (UMD, IBM, Lam Research, Intel, Samsung et al.)
By Technical Paper Link - 18 Jun, 2024 - Comments: 0

A new technical paper titled "Future of plasma etching for microelectronics: Challenges and opportunities" was published by researchers from numerous academic institutions and companies, including University of Maryland, IBM, Arkema, UCLA, Lam Research, Intel Corporation, Samsung, Air Liquide, Sony, and many others. Abstract: "Plasma etching is an essential semiconductor manufacturing techn... » read more

Chip Industry Technical Paper Roundup: May 21
By Linda Christensen - 21 May, 2024 - Comments: 0

New technical papers added to Semiconductor Engineering’s library this week. [table id=227 /] More ReadingTechnical Paper Library home » read more

Controllable Interaction Between Two Hole Spin Qubits In A Conventional Silicon Transistor
By Technical Paper Link - 15 May, 2024 - Comments: 0

A technical paper titled “Anisotropic exchange interaction of two hole-spin qubits” was published by researchers at University of Basel and IBM Research Europe-Zurich. Abstract: "Semiconductor spin qubits offer the potential to employ industrial transistor technology to produce large-scale quantum computers. Silicon hole spin qubits benefit from fast all-electrical qubit control and sweet... » read more

Chip Industry Technical Paper Roundup: May 7
By Liz Allan - 07 May, 2024 - Comments: 0

New technical papers added to Semiconductor Engineering’s library this week. [table id=223 /] More ReadingTechnical Paper Library home » read more

Fundamental Issues In Computer Vision Still Unresolved
By Karen Heyman - 02 May, 2024 - Comments: 1

Given computer vision’s place as the cornerstone of an increasing number of applications from ADAS to medical diagnosis and robotics, it is critical that its weak points be mitigated, such as the ability to identify corner cases or if algorithms are trained on shallow datasets. While well-known bloopers are often the result of human decisions, there are also fundamental technical issues that ... » read more

Metrology For 2D Materials: A Review From The International Roadmap For Devices And Systems (NIST, Et Al.)
By Technical Paper Link - 30 Apr, 2024 - Comments: 0

A technical paper titled “Metrology for 2D materials: a perspective review from the international roadmap for devices and systems” was published by researchers at Arizona State University, IBM Research, Unity-SC, and the National Institute of Standards and Technology (NIST). Abstract: "The International Roadmap for Devices and Systems (IRDS) predicts the integration of 2D materials into h... » read more

Chip Industry Technical Paper Roundup: April 2
By Linda Christensen - 02 Apr, 2024 - Comments: 0

New technical papers recently added to Semiconductor Engineering’s library. [table id=211 /] Find last week’s technical paper additions here. » read more

HW Implementation of Memristive ANNs
By Technical Paper Link - 22 Mar, 2024 - Comments: 0

A new technical paper titled "Hardware implementation of memristor-based artificial neural networks" was published by KAUST, Universitat Autònoma de Barcelona, IBM Research, USC, University of Michigan and others. Abstract: "Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units oper... » read more

Technical Paper Roundup: November 21
By Liz Allan - 21 Nov, 2023 - Comments: 0

New technical papers recently added to Semiconductor Engineering’s library: [table id=167 /] More Reading Technical Paper Library home » read more

Analog In-Memory Cores With Multi-Memristive Unit-Cells (IBM)
By Technical Paper Link - 14 Nov, 2023 - Comments: 0

A technical paper titled “Exploiting the State Dependency of Conductance Variations in Memristive Devices for Accurate In-Memory Computing” was published by researchers at IBM Research-Europe, IBM Research-Albany, and IBM Research-Yorktown Heights. Abstract: "Analog in-memory computing (AIMC) using memristive devices is considered a promising Non-von Neumann approach for deep learning (DL... » read more

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