A Low-Power Differential Temperature Sensor with Chopped Cascode Transistors and Switched-Capacitor Integration
Year: 2025
Type: article
Abstract: Embedded differential temperature sensors can be utilized to monitor the power consumption of circuits, taking advantage of the inherent on-chip electrothermal coupling. Potential applications range from hardware security to linearity, gain/bandwidth calibration, defect-oriented testing, and compensation for circuit aging effects. This paper introduces the use of on-chip differential temperature sensors as part of a wireless Internet of Things system. A new low-power differential temperature sensor circuit with chopped cascode transistors and switched-capacitor integration is described. This design approach leverages chopper stabilization in combination with a switched-capacitor integrator that acts as a low-pass filter such that the circuit provides offset and low-frequency noise mitigation. Simulation results of the proposed differential temperature sensor in a 65 nm complementary metal-oxide-semiconductor (CMOS) process show a sensitivity of 33.18V/°C within a linear range of ±36.5m°C and an integrated output noise of 0.862mVrms (from 1 to 441.7 Hz) with an overall power consumption of 0.187mW. Considering a figure of merit that involves sensitivity, linear range, noise, and power, the new temperature sensor topology demonstrates a significant improvement compared to state-of-the-art differential temperature sensors for on-chip monitoring of power dissipation.
Source: Electronics
Authors: Junyi Yang, Thomas Gourousis, Mengting Yan, Ruyi Ding, Ankit Mittal, Milin Zhang, Francesco Restuccia, Aatmesh Shrivastava, Yunsi Fei, Marvin Onabajo
Institutions: Northeastern University, Analog Devices (United States)
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Awards: G1319719098, G1418398344, G1483026471, G1869219662, G1935467843, G201590018, G2554158150, G2614975528, G2687685902, G3120737655, G3288674344, G3844987702, G3856736044, G4033444091, G4202981357, G4302385606, CCF: SHF: Small: Self-Adaptive Interference-Avoiding Wireless Receiver Hardware through Real-Time Learning-Based Automatic Optimization of Power-Efficient Integrated Circuits, G5114677778, G530518481, G5470303390, G5681543423, G5833460888, G5834599772, G6081317489, G6114848347, G6218331348, G6928493897, G7083697840, G7566824474, EAGER: Side Channels Go Deep - Leveraging Deep Learning for Side-channel Analysis and Protection, G7643492194, Collaborative Research: FuSe: Deep Learning and Signal Processing using Silicon Photonics and Digital CMOS Circuits for Ultra-Wideband Spectrum Perception, G7944348463, High Efficiency Distributed Beamforming RF Energy Transfer using a Closed-loop Energy Receiver, G8223449533, RINGS: Internet of Things Resilience through Spectrum-Agile Circuits, Learning-Based Communications and Thermal Hardware Security, G8711700701, G8927239830, Collaborative Research: SWIFT: AI-based Sensing for Improved Resiliency via Spectral Adaptation with Lifelong Learning, G917360566
OpenAlex ID: W4411215534