A Magnetic Tunnel Junction Based True Random Number Generator with - - PowerPoint PPT Presentation

A Magnetic Tunnel Junction Based True Random Number Generator with Conditional Perturb and Real-Time Output Probability Tracking

Won Ho Choi*, Yang Lv*, Jongyeon Kim, Abhishek Deshpande, Gyuseong Kang, Jian-Ping Wang, and Chris H. Kim

*equal contribution

University of Minnesota, Minneapolis

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Outline of Presentation

• True Random Number Generator (TRNG)
• Magnetic Tunnel Junction (MTJ)
• MTJ-based TRNG
• Conditional perturb scheme
• Real-time output probability tracking
• Conclusions

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An Application of True Random Number Generator (TRNG)

• Generates independent, unpredictable,

nondeterministic, and aperiodic random numbers

• Use random numbers to generate secret keys
• Q. Tang, et. al., CICC, 2014

3

Prior Art of Physical TRNG

• Direct noise amplification from devices

– Random Telegraph Noise (R. Brederlow, ISSCC, 2006) – Resistor thermal noise (V. Kaenel, CICC 2007) – Requires post-processing to achieve sufficient randomness

• ROSC based TRNG (M. Bucci, Tran. on Comp., 2003; Q. Tang, CICC, 2014)

– Harvesting noise from oscillator jitter – Generally requires noise amplification otherwise yield with low efficiency, thus increases design complexity

• Metastability TRNG (C. Tokunaga, JSSC, 2008; S. Mathew, JSSC, 2012)

– Inverter pair driven to metastable state – Requires continuous calibrating loop

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Magnetic Tunnel Junction (MTJ)

• Spin polarized electrons rotate the magnetization

direction of free layer with spin torque

5

Switching Probability of an MTJ

• H. Zhao, et. al., JAP, 2011
• Random thermal fluctuation in an MTJ can be utilized for

generating random bits

• Trade-off relationship between speed, switching energy,

and reliability

• Switching probability is sensitive to operating conditions

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MTJ-Based TRNG

• Unconditional Reset Scheme -
• Applies large reset voltage in every cycles

thereby, adversely effecting on TRNG performance

• S. Yuasa, et. al., IEDM, 2013, concept only

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Proposed Conditional Perturb Scheme

• Perturbs the MTJ according to the previously

sampled MTJ state, thereby eliminating the reset phase

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MTJ Time-to-Breakdown Analysis

• Absence of a reset phase enhances the

lifetime of the MTJ

Failure (%)

9

• C. Yoshida, et al., IRPS, 2009

Fabricated MTJ Device

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• Fabricated MTJ device is used for

demonstration of the MTJ-based TRNG

Measurement Setup

• Random number generator measurement

setup with sub-50 picosecond pulse width resolution.

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Measured Probability

• A small number of segments fail to meet

50± ± ± ±1% probability

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Unconditional reset scheme Conditional perturb scheme

Measured Randomness

• Both schemes show a similar level of

randomness

• The output data fail to pass the frequency and

cumulative sums tests

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Test Pass/Fail

1

Frequency Fail

2

Block frequency

Pass

3

Cumulative Sums Fail

4

Runs Pass

5

Longest-Run-of-Ones Pass

6

Rank Pass

7

FFT Pass

8

Non-overlapping Template Matching

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Serial Pass

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Approximate Entropy Pass

# of segments: 55

Pass

Unconditional reset scheme Conditional perturb scheme

• Simple single-parameter feedback control
• The proposed techniques were implemented

in LabVIEWTM and experimentally verified using a fabricated MTJ device

Real-Time Output Probability Tracking

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Measured Probability and Randomness

• Real-Time Output Probability Tracking-
• Proposed conditional perturb and real-time

probability tracking achieves a good randomness while improving the reliability, speed, and power

Test Pass/Fail

1

Frequency

2

Block frequency

Pass

3

Cumulative Sums

4

Runs Pass

5

Longest-Run-of-Ones Pass

6

Rank Pass

7

FFT Pass

8

Non-overlapping Template Matching

9

Serial Pass

10

Approximate Entropy Pass

Conditional perturb scheme, # of segments: 55

Pass

Raw data after probability tracking

Pass Pass

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TRNG Performance Comparison

• Conditional perturb scheme improves the

speed, switching energy and reliability

*S. Yuasa, et. al., IEDM, 2013

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• It could potentially allow massive generation of

random numbers with negligible circuit overhead

A Possible Application with STT-MRAM

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• We demonstrate for the first time a True

Random Number Generator (TRNG) based on the random switching probability of Magnetic Tunnel Junctions (MTJs)

• Proposed conditional perturb and real-time
• utput probability tracking achieves a good

randomness while improving the reliability, speed, and power

Conclusions

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