Three Dimensional Integration Technology Applied to Neuromorphic Hardware Implementation

Ehsan, M. A.; Zhou, Zhen; Yi, Yang

doi:10.1109/inis.2015.72

Cited by 4 publications

(4 citation statements)

References 21 publications

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“…The usage of TSVs (Figure 12(b)) can be extended to other configurations, as explained by Ehsan et al [112]. In particular, redundant and superfluous dummy TSVs were used as membrane capacitors [113], [114]. Such configurations can enhance the performance without increasing the silicon area.…”

Section: ×mentioning

confidence: 99%

An Electromagnetic Perspective of Artificial Intelligence Neuromorphic Chips

Li,

Ma,

Ahmed

et al. 2023

Electromag. Sci.

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The emergence of artificial intelligence has represented great potential in solving a wide range of complex problems. However, traditional general-purpose chips based on von Neumann architectures face the "memory wall" problem when applied in artificial intelligence applications. Based on the efficiency of the human brain, many intelligent neuromorphic chips have been proposed to emulate its working mechanism and neuron-synapse structure. With the emergence of spiking-based neuromorphic chips, the computation and energy efficiency of such devices could be enhanced by integrating a variety of features inspired by the biological brain. Aligning with the rapid development of neuromorphic chips, it is of great importance to quickly initiate the investigation of the electromagnetic interference and signal integrity issues related to neuromorphic chips for both CMOS-based and memristor-based artificial intelligence integrated circuits. Here, this paper provides a review of neuromorphic circuit design and algorithms in terms of electromagnetic issues and opportunities with a focus on signal integrity issues, modeling, and optimization. Moreover, the heterogeneous structures of neuromorphic circuits and other circuits, such as memory arrays and sensors using different integration technologies, are also reviewed, and locations where signal integrity might be compromised are discussed. Finally, we provide future trends in electromagnetic interference and signal integrity and outline prospects for upcoming neuromorphic devices.

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Section: ×mentioning

confidence: 99%

An Electromagnetic Perspective of Artificial Intelligence Neuromorphic Chips

Li,

Ma,

Ahmed

et al. 2023

Electromag. Sci.

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“…It is now commonly known that 3D integration makes it feasible to achieve high interconnect densities, wide bandwidth, minimum energy consumption and form factors, and the heterogeneous integration of numerous packages [45][46][47][48][49][50][51][52][53]. The superior bandwidth gained by the 3D integration of various memory devices has refueled interest in parallel and sequential HPC technologies, which are regaining popularity in cutting-edge fields like deep learning, neuromorphic computing, networks-on-chip, and 5G/6G communication networks [54][55][56][57][58].…”

Section: Evolution Of Through-hole Technologymentioning

confidence: 99%

Advanced 3D Through-Si-Via and Solder Bumping Technology: A Review

Jang,

Sharma,

Jung

2023

Materials

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Three-dimensional (3D) packaging using through-Si-via (TSV) is a key technique for achieving high-density integration, high-speed connectivity, and for downsizing of electronic devices. This paper describes recent developments in TSV fabrication and bonding methods in advanced 3D electronic packaging. In particular, the authors have overviewed the recent progress in the fabrication of TSV, various etching and functional layers, and conductive filling of TSVs, as well as bonding materials such as low-temperature nano-modified solders, transient liquid phase (TLP) bonding, Cu pillars, composite hybrids, and bump-free bonding, as well as the role of emerging high entropy alloy (HEA) solders in 3D microelectronic packaging. This paper serves as a guideline enumerating the current developments in 3D packaging that allow Si semiconductors to deliver improved performance and power efficiency.

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“…It has also been utilized to stack neuromorphic chips. Through silicon vias (TSVs) are commonly used to physically implement 3D integration approaches for neuromorphic systems [1058], [2066], [2385]- [2387], partially because utilizing TSVs in neuromorphic systems help mitigate some of the issues that arise with using TSVs, such as parasitic capacitance [2388]; however, other technologies have also been utilized in 3D integration, such as microbumps [2389]. 3D integration is commonly used in neuromorphic systems with a variety of other technologies, such as memristors [866], [2390]- [2393], phase change memory [2093], and CMOS-molecular (CMOL) systems [2394].…”

Section: A Communicationmentioning

confidence: 99%

A Survey of Neuromorphic Computing and Neural Networks in Hardware

Schuman¹,

Potok²,

Patton³

et al. 2017

Preprint

176

180

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Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture. This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems. The promise of the technology is to create a brainlike ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities. In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history. We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications. We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled. The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed.

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Three Dimensional Integration Technology Applied to Neuromorphic Hardware Implementation

Cited by 4 publications

References 21 publications

An Electromagnetic Perspective of Artificial Intelligence Neuromorphic Chips

An Electromagnetic Perspective of Artificial Intelligence Neuromorphic Chips

Advanced 3D Through-Si-Via and Solder Bumping Technology: A Review

A Survey of Neuromorphic Computing and Neural Networks in Hardware

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