A New Family of bioSFQ Logic/Memory Cells

Семенов, В.К.; Golden, Evan; Tolpygo, Sergey K.

doi:10.1109/tasc.2021.3138369

Cited by 17 publications

(8 citation statements)

References 26 publications

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“…Fabrication processes used to manufacture these devices are, however, not well established, and the resulting circuits are limited in scale. In [33], an SFQ-based methodology for building neuromorphic networks is proposed, where a bipolar current is used to represent a logic state, which is converted into a train of SFQ pulses for transmission. Stochasticity in neuromorphic SFQ neurons has been studied [17], but to the authors' best knowledge, there are no studies exploring stochasticity in SFQ synapses.…”

Section: Single Flux Quanta Circuitrymentioning

confidence: 99%

“…In the absence of noise current I T , the gating functionality depends deterministically on the applied currents, however in the presence of thermal fluctuations in the applied currents, the balanced comparator exhibits stochastic behavior-a 'grey zone' [34]. A similar approach has been proposed for SFQ based synapses [33], where a C-SQUID [38] is used rather than a balanced comparator.…”

Section: Stochastic-pass Synapsesmentioning

confidence: 99%

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Harnessing stochasticity for superconductive multi-layer spike-rate-coded neuromorphic networks

Edwards,

Krylov,

Friedman

et al. 2024

Neuromorph. Comput. Eng.

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Conventional semiconductor-based integrated circuits are gradually approaching fundamental scaling limits. Many prospective solutions have recently emerged to supplement or replace both the technology on which basic devices are built and the architecture of data processing.Neuromorphic circuits are a promising approach to computing where techniques used by the brain to achieve high efficiency are exploited. Many existing neuromorphic circuits rely on unconventional and useful properties of novel technologies to better mimic the operation of the brain.One such technology is single flux quantum (SFQ) logic -- a cryogenic superconductive technology in which the data are represented by quanta of magnetic flux (fluxons) produced and processed by Josephson junctions embedded within inductive loops.The movement of a fluxon within a circuit produces a quantized voltage pulse (SFQ pulse), resembling a neuronal spiking event. These circuits routinely operate at clock frequencies of tens to hundreds of gigahertz, making SFQ a natural technology for processing high frequency pulse trains.This work harnesses thermal stochasticity in superconducting synapses to emulate stochasticity in biological synapses in which the synapse probabilistically propagates or blocks incoming spikes. The authors also present neuronal, fan-in, and fan-out circuitry inspired by the literature that seamlessly cascade with the synapses for deep neural network construction. Synapse weights and neuron biases are set with bias current, and the authors propose multiple mechanisms for training the network and storing weights. The network primitives are successfully demonstrated in simulation in the context of a rate-coded multi-layer XOR neural network which achieves a wide classification margin. The proposed methodology is based solely on existing SFQ technology and does not employ unconventional superconductive devices or semiconductor transistors, making this proposed system an effective approach for scalable cryogenic neuromorphic computing.

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Section: Single Flux Quanta Circuitrymentioning

confidence: 99%

Section: Stochastic-pass Synapsesmentioning

confidence: 99%

Harnessing stochasticity for superconductive multi-layer spike-rate-coded neuromorphic networks

Edwards,

Krylov,

Friedman

et al. 2024

Neuromorph. Comput. Eng.

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“…The typical current ⟨𝐼𝐼 𝑐𝑐 ⟩ depends on the circuit, acceptable bit error rate, energy dissipation requirements, and other factors. For circuits operating at 4 K, it can be as low as 10 µA, e.g., in neuromorphic circuits processing information stochastically [10]- [12], about 25 µA in RQL circuits [13], about 50 µA in Quantum Flux Parametron (QFP) circuits [14], and as high as ~ 175 µA in RSFQ [1] and ERSFQ [15] circuits if extremely low bit error rates are required. The plot of ( 1) is shown in Fig.…”

Section: A Balancing Densities Of Junctions and Inductorsmentioning

confidence: 99%

Progress Toward Superconductor Electronics Fabrication Process With Planarized NbN and NbN/Nb Layers

Tolpygo

Mallek

Bolkhovsky

et al. 2023

IEEE Trans. Appl. Supercond.

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In order to increase circuit density of superconductor digital and neuromorphic circuits by 10× and reach integration scale of 10 8 Josephson junctions (JJs) per chip, we developed a new fabrication process on 200-mm wafers, using self-shunted Nb/Al-AlOx/Nb JJs and kinetic inductors for cell miniaturization. The process has one layer of JJs, one layer of resistors, and ten fully planarized superconducting layers: 8 niobium layers and two layers of high kinetic inductance materials, Mo2N and NbN, with sheet inductance of 8 pH/sq and 3 pH/sq, respectively. The minimum linewidth of NbN kinetic inductors is 250 nm. NbN films were deposited by two methods: with 𝑻𝑻 𝒄𝒄 ≈15.5 K by reactive sputtering of a Nb target in Ar+N2 mixture; with 𝑻𝑻 𝒄𝒄 in the range from 9 K to 13 K by plasmaenhanced chemical vapor deposition (PECVD) using Tris(diethylamido)(tert-butylimido)niobium(V) metalorganic precursor. PECVD of NbN was investigated to obtain conformal deposition and filling narrow trenches and vias with high depth-to-width ratios, h/w>1, which was not possible to achieve using sputtering and other physical vapor deposition (PVD) methods at temperatures below 200 o C required to prevent degradation of Nb/Al-AlOx/Nb junctions. Nb layers with 200 nm thickness are used in the process layer stack as ground planes to maintain a high level of interlayer shielding and low intralayer mutual coupling, for passive transmission lines with wave impedances matching impedances of JJs, typically ≤50 Ω, and for low-value inductors. NbN and NbN/Nb bilayer are used for cell inductors. Using NbN/Nb bilayers and individual pattering of both layers to form inductors allowed us to minimize parasitic kinetic inductance associated with interlayer vias and connections to JJs as well as to increase critical currents of the vias. Fabrication details and results of electrical characterization of NbN films, wires, and vias, and comparison with Nb properties are given.

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“…While the superconducting neural networks of various kinds are rapidly developed currently [ 22 , 23 , 24 , 28 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ], their complexity is severely limited by the low integration density of superconducting circuits [ 41 , 42 ]. One of the main reasons for this is a comparatively large area (an order of a micron to few tenths of a micron squared) of commonly used superconductor-insulator-superconductor tunnel Josephson junction [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

Superconducting Bio-Inspired Au-Nanowire-Based Neurons

et al. 2022

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High-performance modeling of neurophysiological processes is an urgent task that requires new approaches to information processing. In this context, two- and three-junction superconducting quantum interferometers with Josephson weak links based on gold nanowires are fabricated and investigated experimentally. The studied cells are proposed for the implementation of bio-inspired neurons—high-performance, energy-efficient, and compact elements of neuromorphic processor. The operation modes of an advanced artificial neuron capable of generating the burst firing activation patterns are explored theoretically. A comparison with the Izhikevich mathematical model of biological neurons is carried out.

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A New Family of bioSFQ Logic/Memory Cells

Cited by 17 publications

References 26 publications

Harnessing stochasticity for superconductive multi-layer spike-rate-coded neuromorphic networks

Harnessing stochasticity for superconductive multi-layer spike-rate-coded neuromorphic networks

Progress Toward Superconductor Electronics Fabrication Process With Planarized NbN and NbN/Nb Layers

Superconducting Bio-Inspired Au-Nanowire-Based Neurons

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