qt-puf-quantum-tunneling-iomt

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QT-PUF: Quantum tunneling leakage-based physical unclonable function for implantable IoMT devices. Gate leakage PUF using process-induced CMOS variations with differential readout circuit. Entropy 0.9999998, power 96.04 nW/bit. Use when designing quantum-inspired hardware security for medical devices, implantable IoMT authentication, ultralow-power PUF circuits, or CMOS process-variation-based cryptographic primitives.

hiyenwong By hiyenwong schedule Updated 6/12/2026

name: qt-puf-quantum-tunneling-iomt description: "QT-PUF: Quantum tunneling leakage-based physical unclonable function for implantable IoMT devices. Gate leakage PUF using process-induced CMOS variations with differential readout circuit. Entropy 0.9999998, power 96.04 nW/bit. Use when designing quantum-inspired hardware security for medical devices, implantable IoMT authentication, ultralow-power PUF circuits, or CMOS process-variation-based cryptographic primitives." category: quantum tags: [quantum, iomt, puf, hardware-security, cmos, implantable-devices, tunneling-leakage] arxiv_id: "2605.22113"

QT-PUF: Quantum Tunneling Leakage-Based PUF for IoMT

Description

Design and analyze quantum tunneling leakage-based physical unclonable functions (PUFs) for implantable Internet of Medical Things (IoMT) devices. Leverages quantum-mechanical gate leakage from process-induced variations in standard CMOS to generate unclonable device fingerprints. Achieves near-perfect entropy with ultralow power consumption suitable for implantable medical devices.

Source: arXiv:2605.22113 (Ma, Mohan, Chang — May 2026)

Activation Keywords

  • qt-puf
  • quantum tunneling PUF
  • implantable iomt security
  • gate leakage PUF
  • iomt device authentication
  • quantum medical device security
  • 量子隧穿 PUF
  • 植入式医疗设备安全
  • hardware security medical devices
  • ultralow-power PUF

Core Concepts

Quantum Tunneling Leakage as Entropy Source

  • Mechanism: Gate oxide tunneling current varies due to atomic-level process variations during CMOS fabrication
  • Quantum origin: Tunneling probability is inherently quantum mechanical (Fowler-Nordheim or direct tunneling)
  • Unclonability: Process variations are random and irreproducible, making each device unique
  • No excitation needed: Operates under static bias — unlike RO or arbiter PUFs that need active oscillation

Differential Readout Architecture

  • Pseudo-resistor I-to-V frontend: Converts picoampere-level leakage currents to measurable voltages
  • Differential measurement: Pair-wise comparison eliminates common-mode noise and environmental drift
  • Digital response: Comparator outputs binary PUF response bits from differential voltage comparison

Key Performance Metrics (65nm CMOS)

Metric Value Significance
Entropy 0.9999998 Near-perfect randomness
FHD (Fractional Hamming Distance) 0.5001 Ideal inter-device uniqueness (target: 0.5)
Power 96.04 nW/bit Ultra-low for implantable devices
Energy 19.21 fJ/bit Extremely efficient per response bit
BER < 0.000163 Reliable across 1.0-1.3V, 10-70°C
Operating voltage 0.9-1.3V Compatible with implantable device power budgets
Operating temp 0-100°C Covers human body temp range

Usage Patterns

Pattern 1: IoMT Device Authentication Design

  1. Identify device trust requirements (implantable vs wearable)
  2. Assess power budget constraints (nW-level for implantable)
  3. Design gate-leakage PUF cell array with differential readout
  4. Validate entropy and FHD through simulation/characterization
  5. Implement challenge-response protocol for device authentication

Pattern 2: PUF Selection for Medical Devices

  1. Evaluate PUF candidates: memory-based, RO, arbiter, vs. tunneling-leakage
  2. For ultralow-power implantable: prefer QT-PUF (no active excitation needed)
  3. For higher-power wearable: RO/arbiter PUFs may suffice
  4. Consider environmental stability (temperature, voltage variation)
  5. Verify BER meets target (< 0.1% for clinical reliability)

Pattern 3: Quantum-Inspired Security Analysis

  1. Identify quantum mechanical effects in device behavior
  2. Model tunneling current variations using quantum transport equations
  3. Assess entropy source quality through statistical analysis
  4. Compare against classical entropy sources (thermal noise, jitter)
  5. Evaluate resistance to modeling attacks and side-channel analysis

Implementation Guidelines

QT-PUF Cell Design

CMOS Gate Structure → Tunneling Current (pA level)
                          ↓
              Pseudo-resistor I-to-V Converter
                          ↓
              Differential Amplifier (pair-wise)
                          ↓
              Comparator → Digital Response Bit

Key Design Parameters

  • Transistor sizing: Minimum-size devices maximize process variation effects
  • Pair matching: Matched pairs for differential measurement improve stability
  • Readout sensitivity: Pseudo-resistor values tuned for picoampere-level currents
  • Temperature compensation: Differential architecture inherently compensates for drift

Pitfalls

  • BER increases at voltage extremes: Below 1.0V or above 1.3V, BER degrades significantly
  • Temperature limits: Above 70°C, BER increases (though device survives to 100°C)
  • Aging effects: Gate oxide degradation over time may shift leakage characteristics
  • Process node dependency: 65nm has significant gate leakage; smaller nodes may have different behavior
  • Not suitable for high-throughput applications: Static readout limits response generation speed

Related Skills

  • post-quantum-iot-healthcare (PQC migration for IoT healthcare systems)
  • post-quantum-secure-pharmacovigilance (PQC for healthcare data pipelines)
  • quantum-resistant-networks (post-quantum network architecture)
Install via CLI
npx skills add https://github.com/hiyenwong/ai_collection --skill qt-puf-quantum-tunneling-iomt
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