A High-Frequency CMOS Meminductor Emulator for Spiking Neuron

This research introduces a high-frequency CMOS meminductor, utilizing operational transconductance amplifiers and two grounded capacitors, thereby avoiding traditional components such as memristors, multipliers, and resistors. The design stands out for its simplicity and electronic adjustability, which facilitates control of the induced flux by altering the transconductance gain. We conducted transistor-level simulations with 180 nm CMOS technology to evaluate the meminductor’s performance across various parameters including voltage, frequency, temperature, process corner variations, and monte carlo simulations. These extensive tests confirm the durability and adaptability of the emulator across diverse conditions. The emulators operate up to 20 MHz with a remarkably low power consumption of just 0.44 mW, showcasing considerable efficiency. The practical applications of the proposed meminductor include advanced uses in developing leaky integrate and fire neuron. Additionally, we successfully fabricated the first physical meminductor emulator chip, which occupies a die area of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2032.7~\mu $ </tex-math></inline-formula>m2 using a 180 nm CMOS process. The integration of these fabrication results helps corroborate the theoretical derivations presented. This convergence of practical fabrication and theoretical insight signifies a major leap in meminductor technology, hinting at wide-ranging future applications in electronic systems.

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Institutions

Publication Details

Journal
IEEE Transactions on Circuits and Systems I Regular Papers
Published
2024-09-16
DOI
https://doi.org/10.1109/tcsi.2024.3454553
Citations
7
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
1.46

Funders

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article

A High-Frequency CMOS Meminductor Emulator for Spiking Neuron

Hyunkeun Lee, Ankur Singh, Dowon Kim, Byung-Geun Lee
7 citations
IEEE Transactions on Circuits and Systems I Regular Papers
Advanced Memory and Neural Computing
1.46
article

A High-Frequency CMOS Meminductor Emulator for Spiking Neuron

Hyunkeun Lee, Ankur Singh, Dowon Kim, Byung-Geun Lee
article en
7 citations

Abstract

This research introduces a high-frequency CMOS meminductor, utilizing operational transconductance amplifiers and two grounded capacitors, thereby avoiding traditional components such as memristors, multipliers, and resistors. The design stands out for its simplicity and electronic adjustability, which facilitates control of the induced flux by altering the transconductance gain. We conducted transistor-level simulations with 180 nm CMOS technology to evaluate the meminductor’s performance across various parameters including voltage, frequency, temperature, process corner variations, and monte carlo simulations. These extensive tests confirm the durability and adaptability of the emulator across diverse conditions. The emulators operate up to 20 MHz with a remarkably low power consumption of just 0.44 mW, showcasing considerable efficiency. The practical applications of the proposed meminductor include advanced uses in developing leaky integrate and fire neuron. Additionally, we successfully fabricated the first physical meminductor emulator chip, which occupies a die area of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2032.7~\mu $ </tex-math></inline-formula>m2 using a 180 nm CMOS process. The integration of these fabrication results helps corroborate the theoretical derivations presented. This convergence of practical fabrication and theoretical insight signifies a major leap in meminductor technology, hinting at wide-ranging future applications in electronic systems.

IEEE Transactions on Circuits and Systems I Regular PapersVol. 72(3)
Korea University (KR), Gwangju Institute of Science and Technology (KR)
Ministry of Science and ICT, South Korea
Affordable and clean energy
Openalex Percentile: Top 18%
Advanced Memory and Neural Computing
1.46
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