Litcius/Paper detail

Design of Interface Circuits and Lightweight PUF for TMR Sensors

Xiangyu Li, Pengjun Wang, Gang Li, Li Ni, Yuejun Zhang

2023IEEE Sensors Journal23 citationsDOI

Abstract

The micromagnetometers with high-resolution digital output are widely used in military and civilian fields. We proposed a novel high-precision interface circuit with an optimized chopper technique and switched-capacitor (SC) modulator for tunneling magnetoresistance (TMR) sensors. This work also proposes a novel method to create a lightweight physically unclonable function (PUF) by using existing TMR devices. The sigma-delta modulator converts the sensor signal into a robust digital output and maintains the signal-to-noise ratio (SNR) of the front-end circuit. We also take advantage of inherent variations of TMR sensors to generate PUF responses that are similarly unique and unclonable. The interface circuit is fabricated by a 0.35- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process from the Shanghai Huahong foundry. The active area of ASIC is only about <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\times2.7$ </tex-math></inline-formula> mm. The interface circuit can achieve an SFDR of 120 dB and an SNR of 98 dB at a sampling frequency of 200 kHz. The TMR magnetometers were tested in an environment of three-layer magnetic shielding. Our proposed PUF is also tested in terms of uniqueness and reliability.

Topics & Concepts

Delta-sigma modulationCMOSTunnel magnetoresistanceElectronic engineeringMagnetoresistive random-access memoryApplication-specific integrated circuitElectrical engineeringComputer scienceEngineeringPhysicsComputer hardwareFerromagnetismRandom access memoryQuantum mechanicsPhysical Unclonable Functions (PUFs) and Hardware SecurityIntegrated Circuits and Semiconductor Failure AnalysisAdvanced Memory and Neural Computing