Abstract

Minimum energy per operation is typically achieved in the subthreshold region where low speed and low robustness are two challenging problems. This paper studies the impact of back biasing (BB) schemes on these features for 28 nm FDSOI technology at three levels of abstraction: gate, library and IP. We show that forward BB (FBB) can help cover a wider design space in terms of the optimal frequency of operation while keeping minimum energy. Asymmetric BB between NMOS and PMOS can mitigate the effect of systematic mismatch on the minimum energy point (MEP) and robustness. With optimal asymmetric BB, we achieve either a MEP reduction up to 18% or a 36× speedup at the MEP. At the IP level, we confirm the MEP configurability with BB with synthesis results of microcontrollers at 0.35 V.We show that the use of a mix of overdrive FBB voltages further improves the energy efficiency. Compared to bulk 65 nm CMOS, we were able 28 nm FDSOI to reduce the energy per cycle by 64% or to increase the frequency of operation by 7×, while maintaining energy per operation below 3 µW/MHz over a wide frequency range.

Highlights

  • With the accelerating expansion of ultra-low-power computing and energy-autonomous systems requested by the vision of the Internet-of-Things, the need for compact battery-less wireless sensor nodes with consequent embedded data processing abilities is getting stronger [1]

  • We only considered low VT (LVT) MOSFETs in 28 nm FDSOI for speed concerns and considered different forward BB (FBB) voltages applied to FDSOI

  • We showed that 28-nm UTBB FDSOI

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Summary

Introduction

With the accelerating expansion of ultra-low-power computing and energy-autonomous systems requested by the vision of the Internet-of-Things, the need for compact battery-less wireless sensor nodes with consequent embedded data processing abilities is getting stronger [1]. Operating with such an aggressively scaled down supply voltage (VDD ) can lead to energy savings up to 10× [4]. As shown in [5], subthreshold FDSOI circuits are emerging and can lead to very high energy efficiency This scaling is limited by two factors: the target frequency constraint and the robustness constraint [6]. In addition to its use for trading the performance for energy efficiency, body biasing has already been proposed on bulk technology to mitigate random VT mismatch under process variation (PV) in [14].

Back Biasing at the Device Level
Scaling and Back Biasing Impact on Frequency and Energy Efficiency
Delay Equalization and Back Biasing Compensation Schemes
Impact of Back Biasing Compensation Schemes on the Minimum Energy Point
Impact of Back Biasing Compensation Schemes on Robustness
Back Biasing Analysis at the Standard Cell Library Level
Back Biasing Analysis at the IP Level
Findings
Conclusions
Full Text
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