Analysis of Neural Spike Signal Transmission Characteristics in 3-D Heterogeneous Integration

Yu, Heyuan; Sun, Zhe; Lei, Xiaoyong; Huo, Shuyun; Li, Yan; Fang, Lidan; Xu, Shaojie; Li, Er‐Ping; Li, Bingheng

doi:10.1109/apemc53576.2022.9888648

Cited by 1 publication

(1 citation statement)

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“…The massive parallelism between the adjacent layers in the 3D structure due to the abundant TSVs imposes EMI and SI issues such as crosstalk and electromagnetic coupling. For instance, the presence of adjacent signal TSVs (Figure 12(c)) [115] imposes electromagnetic field coupling as a result of the strong mutual capacitance and inductance between the TSVs. Moreover, the discontinuous structure (Figure 12(d)) and the bump formed at the interconnection of the TSV and redistribution layer (RDL) may lead to signal loss [116].…”

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%