Body Bias Control for Renewable Energy Source with a High Inner Resistance

Keita Azegami, Hayate Okuhara, Hideharu Amano

Research output: Contribution to journalArticle

Abstract

Sensor nodes used in Internet of Things (IoT) are required to work extremely long time without replacing battery. Natural renewable energy such as a solar battery is a hopeful candidate for such nodes. Here, an energy model for operating an SOI (Silicon on Insulator) device with a solar battery including a large inner resistance is proposed, and applied to a micro-controller V850E-star and an accelerator CMA-SOTB2. Unlike the ideal case, the maximum operational frequency was achieved with a relatively strong reverse bias (from -1.2V to -0.6V), since the increasing leakage current degrades Vdd . It appears that under the room light with a large inner resistance, the strong reverse bias is effective. With the bright light, a relatively weak reverse bias is advantageous. The proposed model is appeared to be useful to estimate the appropriate body bias voltage both for V850E-star and CMA-SOTB2. In the V850E-star the absolute values were different from the real chip, while they were well matched when CMA-SOTB2 was used under the low illuminance.

Original languageEnglish
JournalIEEE Transactions on Multi-Scale Computing Systems
DOIs
Publication statusAccepted/In press - 2018 Apr 16

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Stars
Solar cells
Bias voltage
Sensor nodes
Leakage currents
Particle accelerators
Silicon
Controllers
Internet of things

Keywords

  • Batteries
  • body biasing
  • Computational modeling
  • Current measurement
  • Immune system
  • inner resistance
  • Leakage currents
  • Low power computing
  • Mathematical model
  • Renewable energy sources
  • Silicon on Thin BOX (SOTB)
  • Solar battery

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Hardware and Architecture

Cite this

Body Bias Control for Renewable Energy Source with a High Inner Resistance. / Azegami, Keita; Okuhara, Hayate; Amano, Hideharu.

In: IEEE Transactions on Multi-Scale Computing Systems, 16.04.2018.

Research output: Contribution to journalArticle

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