Evaluation of Power-Clock Waveforms for Positive Feedback Adiabatic Logic in 16 nm FinFET Technology

📅 2026-09-17
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研究在16nm FinFET技术中,通过优化正反馈绝热逻辑(PFAL)的电源时钟波形形状、幅度和频率,以降低能耗。与传统CMOS相比,可实现高达5.3倍的能效提升。
📝 Abstract
Adiabatic logic can recover part of the energy stored on load capacitances through quasi-reversible switching, but its waveform-optimized operation in FinFET technology and at multi-GHz frequencies remains underexplored. This work investigates Positive Feedback Adiabatic Logic (PFAL) in the TSMC 16 nm FinFET process. A functionally complete PFAL gate library is designed, verified, and characterised using energy-delay product optimization over power-clock amplitude, frequency, and waveform shape. The power-clock sweep shows that the minimum-energy waveform approaches a triangular shape rather than a conventional trapezoid. The optimized single-gate PFAL cells achieve up to 3.83x lower energy than equivalent static CMOS gates, while sinusoidal excitation improves energy by up to 1.32x compared with trapezoidal excitation and extends the valid operating range. The library is then used to construct larger combinational circuits, including a 2:1 multiplexer, a 4-bit ripple carry adder, and a 4-bit Brent-Kung carry look-ahead adder. The carry look-ahead adder reaches a gain of up to 5.3x compared with the static CMOS energy estimate for the triangular power-clock.
Problem

Research questions and friction points this paper is trying to address.

Adiabatic Logic
FinFET Technology
Waveform Optimization
Positive Feedback Adiabatic Logic (PFAL)
Innovation

Methods, ideas, or system contributions that make the work stand out.

Positive Feedback Adiabatic Logic (PFAL)
waveform optimization
triangular waveform
energy efficiency
FinFET technology
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Maciej Szymon Pyrzowski
NanoComputing Research Lab, Electrical Engineering Department, Eindhoven University of Technology, Eindhoven, The Netherlands
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Franciszek Łukowski
NanoComputing Research Lab, Electrical Engineering Department, Eindhoven University of Technology, Eindhoven, The Netherlands
Aida Todri-Sanial
Aida Todri-Sanial
Full Professor, TU Eindhoven | Director of Research, CNRS
nanoelectronicsunconventional computingquantum computingoscillatory neural networks