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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 1
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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

1

Outline of Presentation

• True Random Number Generator (TRNG)

• Magnetic Tunnel Junction (MTJ)

• MTJ-based TRNG

• Conditional perturb scheme

• Real-time output probability tracking

• Conclusions

2

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

4

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 conditions6

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

7

Proposed Conditional Perturb Scheme

• Perturbs the MTJ according to the previously

sampled MTJ state, thereby eliminating the

reset phase

8

MTJ Time-to-Breakdown Analysis

• Absence of a reset phase enhances the

lifetime of the MTJ

Fail

ure

(%

)

9

C. Yoshida, et al., IRPS, 2009

Fabricated MTJ Device

10

• 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.11

Measured Probability

• A small number of segments fail to meet

50±±±±1% probability

12

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 13

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

8Non-overlapping

Template Matching

9 Serial Pass

10 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

14

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

8Non-overlapping

Template Matching

9 Serial Pass

10 Approximate Entropy Pass

Conditional perturb scheme, # of segments: 55

Pass

Raw data after probability tracking

Pass

Pass

15

TRNG Performance Comparison

• Conditional perturb scheme improves the

speed, switching energy and reliability

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

16

• It could potentially allow massive generation of random numbers with negligible circuit overhead

A Possible Application with STT-MRAM

17

• 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

output probability tracking achieves a good

randomness while improving the reliability,

speed, and power

Conclusions

18


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