Design of an RRAM-Based Joint Model for Embedded Cellular Smartphone Self-Charging Device

With the development of embedded electronic devices, energy consumption has become a significant design issue in modern systems-on-a-chip. Conventional SRAMs cannot maintain data after powering turned off, limiting their use in applications such as battery-powered smartphone devices that require non-volatility and no leakage current. RRAM devices are recently used extensively in applications such as self-charging wireless sensor networks and storage elements, owing to their intrinsic non-volatility and multi-bit capabilities, making them a potential candidate for mitigating the von Neumann bottleneck. We propose a new RRAM-based hybrid memristor model incorporated with a permanent magnet. The proposed design (1T2R) was simulated in Cadence Virtuoso with a 1.5 V power supply, and the finite-element approach was adopted to simulate magnetization. This model can retain the data after the power is off and provides fast power on/off transitions. It is possible to charge a smartphone battery without an external power source by utilizing a portable charger that uses magnetic induction to convert mechanical energy into electrical energy. In an embedded smartphone self-charging device this addresses eco-friendly concerns and lowers environmental effects. It would lead to the development of magnetic field-assisted embedded portable electronic devices and open the door to new types of energy harvesting for RRAM devices. Our proposed design and simulation results reveal that, under usual conditions, the magnet-based device provide a high voltage to charge a smartphone battery.

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Publication Details

Journal
Micromachines
Published
2025-09-28
DOI
https://doi.org/10.3390/mi16101101
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Design of an RRAM-Based Joint Model for Embedded Cellular Smartphone Self-Charging Device

Abhinav Vishwakarma, Matej Komelj, Michael Huebner, Anubhav Vishwakarma et al.
Micromachines
Advanced Memory and Neural Computing
article

Design of an RRAM-Based Joint Model for Embedded Cellular Smartphone Self-Charging Device

Abhinav Vishwakarma, Matej Komelj, Michael Huebner, Anubhav Vishwakarma, Santosh Kumar Vishvakarma
article en

Abstract

With the development of embedded electronic devices, energy consumption has become a significant design issue in modern systems-on-a-chip. Conventional SRAMs cannot maintain data after powering turned off, limiting their use in applications such as battery-powered smartphone devices that require non-volatility and no leakage current. RRAM devices are recently used extensively in applications such as self-charging wireless sensor networks and storage elements, owing to their intrinsic non-volatility and multi-bit capabilities, making them a potential candidate for mitigating the von Neumann bottleneck. We propose a new RRAM-based hybrid memristor model incorporated with a permanent magnet. The proposed design (1T2R) was simulated in Cadence Virtuoso with a 1.5 V power supply, and the finite-element approach was adopted to simulate magnetization. This model can retain the data after the power is off and provides fast power on/off transitions. It is possible to charge a smartphone battery without an external power source by utilizing a portable charger that uses magnetic induction to convert mechanical energy into electrical energy. In an embedded smartphone self-charging device this addresses eco-friendly concerns and lowers environmental effects. It would lead to the development of magnetic field-assisted embedded portable electronic devices and open the door to new types of energy harvesting for RRAM devices. Our proposed design and simulation results reveal that, under usual conditions, the magnet-based device provide a high voltage to charge a smartphone battery.

MicromachinesVol. 16(10)
Jožef Stefan Institute (SI), Brandenburg University of Technology Cottbus-Senftenberg (DE), Indian Institute of Technology Indore (IN)
Institut "Jožef Stefan"
Openalex Percentile: Top 74%
Advanced Memory and Neural Computing
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Design of an RRAM-Based Joint Model for Embedded Cellular Smartphone Self-Charging Device — Abhinav Vishwakarma, Matej Komelj, et al. · Micromachines (2025) | TGRS Research Map | TGRS