thermal modeling and design on smartphones with heat pipe...

1/262017 International Conference on Computer Aided Design

Thermal Modeling and Design on Smartphones

with Heat Pipe Cooling Technique

Hong-Wen Chiou, Yu-Min Lee, Hsuan-Hsuan Hsiao, Liang-Chia Cheng

National Chiao Tung University, Taiwan

Industrial Technology Research Institute, Taiwan

2/26

Outline

• Introduction

• Thermal modeling on smartphone with heat pipe

• Automatic heat pipe routing on smartphone

• Experimental results

• Conclusions

3/26

Motivation (1/2)

• The sales of smartphones and tablets(handheld devices) have surpassedthat of PCs in 2011

• Relative high power consumption onsmall-form factor

• Why consider thermal issues on smartphones– Higher performance on application processors (AP) (Apple

A4~A10, Samsung Exynos 4~9 Series, and Qualcomm Snapdragon

200~800 Series)

– Cooling techniques

• PC: Active cooling (Fans, forced convection)

• Smartphone: Passive cooling (Free air, free convection)

[1]

4/26

Motivation (2/2)

• Targets that should be considered in thermal constraints

– AP, which contains CPU and GPU

– Skin/screen, which is usually touched by hands

• Thermal solutions for passive cooling

– Graphite sheet (with high planner heat transfer ability)

– Heat pipe (with higher heat transfer ability)

www.alibaba.com Heat pipe [2, 2016]

5/26

Pros of Heat Pipe on Handheld Devices

• Heat pipe ─ Superconductors in thermal

– High-speed cooling (higher heat transfer ability)

• Thermal conductivity is up to 4000 W/m2K while copper is

only 400 W/m2K

– Transferring heat for specific chips (such as AP) to

cold region

Cool Mater Co., Ltd. Dutch knowledge center

6/26

Principle of Heat Pipe Heat Transfer

Dutch knowledge center

Three main sections: evaporator, adiabatic section, and condenser

1) The heat from chip is transferred to liquid water on evaporator

2) Liquid water is vaporized into hot water vapor

3) Hot water vapor flows to condenser (temperature gradient)

4) Hot water vapor condenses to liquid water and flow into wick structure

5) Liquid water flow to evaporator due to capillarity

6) Repeating above procedures

Chip Cold region

Copper

Wick structure (liquid water)

Water vapor

7/26

Smartphones with Heat Pipes

Brand name Processor Price Date Heat pipe

NEC

Medias X

Samsung

Exynos 5 OctaNA 2014 Yes, first one

Sony

Xperia Z5

Qualcomm

Snapdragon 810$750 2015.10 Yes

Microsoft

Lumia 950 XL

Qualcomm

Snapdragon 808$550 2016.04 Yes

Samsung

Galaxy S7

Samsung

Exynos 8890$500 2016.04 Yes

Samsung

Galaxy S8

Samsung

Exynos 8895$600 2017.05 Yes

LG

G6

Qualcomm

Snapdragon 821$800 2017.03 Yes

Nokia

8

Qualcomm

Snapdragon 835$550 2017.08 Yes

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Heat Pipe on Smartphones (1/2)

Samsung Galaxy S7 [2, 2016]

Sony Xperia Z2 [3, 2015]

Liquid heat-pipe cooling

9/26

Heat Pipe on Smartphones (2/2)

Nokia 8 [5, 2017]

10/26

Why Need Heat Pipe

Compact Modeling and Routing Design?

• Thermal conductivity of vapor in heat pipe, with

complicated properties such as two-phase heat

transfer on the boundary, leads to commercial

tools such as ANSYS Fluent be inefficient (more

accurate but time consuming)

• The heat pipes on smartphones are manually

designed. Automatic heat pipe design may be

more efficient (better quality on design time and

heat dissipating) than manual heat pipe design

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Contributions

• The temperature error of concerned area (AP, skin,

screen) between our thermal simulator and

ANSYS Fluent is less than 7.1% with speedup

over 1000 times

• The proposed optimization algorithm for heat pipe

design can reduce temperature 7℃ than the

smartphone with no heat pipe

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Thermal Simulation Flow of Smartphones

with Bended Heat Pipe

Effective thermal conductivity

for vapor in heat pipe

Accurate thermal map on dies,

skin, and screen

Build resistor network

13/26

Thermal Simulation on

Smartphone with Heat Pipe

• Part I: Bended heat pipe thermal model

• Part II: Compact thermal model for solid structure

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Part I: Bended Heat Pipe Thermal Model

Vapor effective thermal conductivity

→ 𝑔 𝑇1 − 𝑇2 = 𝑝 and 𝑔 =𝑘𝐴

𝐿

→ 𝑇1 − 𝑇2 = ∆𝑇 = 2.5℃ [6]

→ 𝑝 = design power around heat pipe

→ Efficiency decreasing rate = 20% [7]

→ 𝑘𝑒𝑓𝑓 =𝐿𝑝

∆𝑇𝐴∗ ሺ1 − Efficiency decreasing

Heat source

Wall

Wicked material

Fluid (water vapor)

𝐿1

𝐿2

15/26

Part II: Compact Model for

Solid Structure and Vapor of Heat Pipe

• Integrate the thermal conductivity of vapor in the heat pipe (P.14) into compact

model

• By using finite difference method, the heat transfer governing equation 𝑮𝑻 = 𝑷in matrix form

• By LU factorization technique on 𝑮, the temperature 𝑻 can be solved by forward

and backward substitution

Voltage (𝑽) Temperature (𝑻)

Current (𝑰) Power (𝑷)

Circuit simulation Thermal simulation

MNA

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Heat Pipe Routing Design Flow

Part I:

Weight function building

Part II:

Inconsistent maze routing

procedures

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Part I: Weight Function Building

• Thermal simulations w/ and w/o heat pipe from 𝑛𝑏 (number of bends) = 0~5– In each situation, it considers 40 routing patterns

• Initial weighted function

𝑇𝑟𝑗,𝑛𝑏 = 𝑇𝑟𝑗 ⋅ 𝑒−𝛼𝑛𝑏⋅𝑑𝑟𝑗+𝛽𝑛𝑏

Here, 𝑇𝑟𝑗,𝑛𝑏 is the rising temperature of 𝑟𝑗, and 𝑑𝑟𝑗 is the

distance between 𝑟𝑗 and the routing source grid (hot spot point)

𝛼𝑛𝑏 and 𝛽𝑛𝑏 are fitting constants

– The function will be fit by using Matlab with maximum error less than 15%

• Inconsistent weight function𝐷𝑇ℎ𝑒𝑟𝑚𝑊𝑟𝑗,𝑛𝑏 = 𝑇𝑟𝑗,𝑛𝑏

18/26

Part II: Inconsistent Maze Routing

Assign weights on all routing grids

Inconsistent maze routing with dynamic weight

𝐻𝑒𝑎𝑡𝐴𝑐𝑐 = 𝐻𝑒𝑎𝑡𝐴𝑐𝑐 + 𝐷𝑇ℎ𝑒𝑟𝑚𝑊𝑟𝑗,𝑛𝑏

Note: 𝐻𝑒𝑎𝑡𝐴𝑐𝑐 is the accumulated heat from source

grid to this grid

Find maximum 𝐻𝑒𝑎𝑡𝐴𝑐𝑐

19/26

Example of Inconsistent Maze Routing

Wave Propagation

No

pipe

Zero

bend

Zero

bend

One

bend

20/26

Experimental Setup

The heat pipe is with grooved wick structure.

GS4: Samsung Galaxy 4 [6]

N5: Google Nexus 5 [7]

MDP: Qualcomm Mobile Development Platform [6]

Figure 1: Samsung Galaxy 4 GS4 - Snapdragon 600 [6]

Figure 3: Qualcomm Mobile Development Platform - MSM 8660 [6]

Figure 2: Google Nexus 5 GS4 - Snapdragon 800 [7]

21/26

Test Case Fluent The Proposed Thermal Simulator

NameBends of

Heat Pipe

Time

(sec)

Time

(sec)

𝑒𝑚𝑎𝑥 (%) 𝑒𝑎𝑣𝑔 (%) Speedup

(×)Chips SK SC Chips SK SC

MDP-C1

4

1650.00 1.34 3.22 4.75 6.73 2.03 3.57 5.68 1231.34

MDP-C2 1663.00 1.38 2.20 6.58 6.39 1.17 5.20 5.58 1205.07

MDP-C3 1654.00 1.27 1.78 5.16 4.97 1.12 3.59 3.67 1302.36

MDP-C4 1665.00 1.36 1.88 6.99 5.70 1.15 5.39 4.84 1224.26

MDP-C5 1669.00 1.28 2.96 6.58 6.13 1.65 6.01 5.06 1303.91

GS4-C1

4

1658.00 1.27 2.52 4.88 6.10 1.70 4.02 5.22 1305.51

GS4-C2 1670.00 1.30 2.41 4.01 6.74 1.64 3.08 5.83 1284.62

GS4-C3 1660.00 1.27 3.58 4.77 6.08 2.13 3.67 5.04 1307.09

GS4-C4 1659.00 1.41 2.12 6.05 6.38 1.12 5.11 5.35 1176.60

GS4-C5 1663.00 1.43 2.75 6.82 6.80 1.67 5.77 5.85 1162.94

N5-C1

3

1667.00 1.30 2.24 4.97 6.53 1.83 4.27 5.49 1282.31

N5-C2 1670.00 1.40 2.04 4.31 7.10 1.73 3.41 6.30 1192.86

N5-C3 1670.00 1.28 3.35 4.43 6.72 2.98 4.15 5.62 1304.69

N5-C4 1657.00 1.42 1.92 6.70 5.94 1.60 5.62 5.97 1166.90

N5-C5 1651.00 1.45 2.55 6.81 6.36 1.68 6.39 5.96 1138.62

MDP: Qualcomm Mobile Development Platform; GS4: Samsung Galaxy 4

The results are with the max./avg. temperature of application processor (AP), skin, and screen.

Maximum error: 3.58%, 6.99%, and 7.10% (AP, skin, screen)

Speedup is with three order of magnitude (1000×)

Accuracy & Efficiency of Our Thermal Simulator

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Thermal Map by Our Thermal Simulator

ANSYS

Our Thermal

Simulator

Nexus 5Galaxy S4

23/26

Temperature Reduction with

Heat Pipe Designs

NHP

Heat Pipe Design

SP-MR I-MR Exhausted Method SP-MR I-MR Exhausted Method

Test

Case

Max Temperature

(℃ )Max Temperature (℃) Max Temperature Reduction (℃)

AP SK SC AP SK SC AP SK SC AP SK SC AP SK SC AP SK SC AP SK SC

MDP-C1 58.40 38.66 37.95 52.55 36.53 32.40 48.56 34.84 31.51 48.34 34.52 31.20 5.84 2.13 5.55 9.84 3.82 6.44 10.06 4.14 6.76

MDP-C2 58.01 38.69 37.21 54.62 36.22 35.40 50.40 34.01 33.84 50.16 33.74 33.53 3.39 2.47 1.81 7.61 4.68 3.38 7.85 4.95 3.68

MDP-C3 44.73 33.98 36.65 40.51 33.57 34.59 37.56 31.36 31.28 37.30 31.13 30.94 4.21 0.41 2.06 7.17 2.62 5.37 7.43 2.85 5.71

MDP-C4 42.24 32.15 31.89 38.31 31.49 31.12 34.99 29.43 28.66 34.67 29.18 28.30 3.92 0.67 0.77 7.24 2.73 3.24 7.57 2.97 3.60

MDP-C5 38.95 30.50 30.46 37.62 29.50 30.01 33.14 27.80 28.24 32.91 27.48 28.02 1.33 1.00 0.45 5.82 2.70 2.22 6.04 3.02 2.44

GS4-C1 73.44 42.96 42.90 71.71 39.69 39.32 68.18 37.90 37.07 67.96 37.55 36.87 1.72 3.27 3.57 5.26 5.06 5.82 5.48 5.41 6.03

GS4-C2 71.25 42.91 42.19 67.78 39.22 39.11 63.00 38.04 36.05 62.63 37.72 35.83 3.47 3.69 3.07 8.25 4.87 6.14 8.62 5.20 6.36

GS4-C3 67.35 39.19 39.72 65.28 36.00 35.12 60.68 33.93 32.81 60.28 33.66 32.60 2.08 3.19 4.60 6.68 5.26 6.91 7.07 5.53 7.12

GS4-C4 65.50 39.04 37.96 62.86 36.36 34.90 59.72 33.27 32.98 59.48 32.91 32.70 2.64 2.68 3.06 5.78 5.77 4.98 6.02 6.13 5.26

GS4-C5 66.74 40.16 38.55 63.55 35.72 34.88 59.05 33.48 32.76 58.73 33.26 32.38 3.19 4.44 3.67 7.69 6.68 5.79 8.01 6.90 6.17

N5-C1 75.96 45.60 46.09 74.72 43.05 42.07 70.42 41.35 40.44 70.17 40.96 40.16 1.24 2.56 4.02 5.54 4.25 5.65 5.79 4.64 5.93

N5-C2 73.57 45.77 44.59 70.13 41.79 41.33 65.70 40.66 39.37 65.34 40.33 39.01 3.44 3.98 3.26 7.87 5.10 5.22 8.23 5.44 5.58

N5-C3 70.28 42.57 42.34 67.91 38.16 37.62 63.07 36.09 35.90 62.71 35.80 35.65 2.37 4.41 4.72 7.20 6.48 6.44 7.58 6.77 6.69

N5-C4 68.25 41.53 41.52 66.27 38.36 37.54 62.03 36.28 35.76 61.64 35.97 35.55 1.98 3.17 3.98 6.22 5.26 5.76 6.61 5.56 5.97

N5-C5 69.73 43.28 40.63 66.49 39.32 38.35 62.14 36.45 35.04 61.75 36.25 34.64 3.23 3.97 2.28 7.59 6.83 5.59 7.98 7.03 5.99

Avg. 2.94 2.80 3.12 7.02 4.81 5.26 7.36 5.10 5.55

The grid with lowest temperature in “no heat pipe (NHP)” result is set as sink grid in

shortest path maze routing (SPMR)

SPMR cases can reduce 3 oC in average more than NHP

Proposed heat pipe routing method can reduce 7 oC in average more than NHP cases

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Thermal Map & Heat Pipe Routing Path

No heat pipe

Shortest path maze routing

Inconsistent maze routing w/

dynamic thermal weight

Exhausted searching method

Galaxy S4 Nexus 5

25/26

Conclusion

• The experimental results have verified that the

proposed thermal simulator can accurately (less

than 7.1%) and efficiently (1000×) estimate the

temperatures of smartphones

• This work also developed an inconsistent thermal-

driven maze routing flow for routing the heat pipe

to reduce the operating temperature (up to 7℃ on

AP) of a smartphone

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Thank you for listening

Q & A

27/26

Reference

[1]http://www.pocket-lint.com/news/120309-best-smartphones-2017-the-best-

hones-available-to-buy-today

[2]http://mashable.com/2016/03/09/samsung-galaxy-s7-torn-down/

[3]http://www.youmobile.org/blogs/entry/Sony-Xperia-Z2-to-include-liquid-

cooling-Technology-Inside

[4]https://www.gsmarena.com/lg_g6_disassembly_video_reveals_advanced_heat_

pipe_sealedin_lipo_battery-news-23635.php

[5]http://nokiamob.net/2017/09/01/video-inside-the-nokia-8-and-the-passive-

ooling-system-btekt-ifa2017/

[6] Q. Xie, M. J. Dousti, and P. M, “Therminator: a thermal simulator

forsmartphones producing accurate chip and skin temperature maps,” in Int. Symp.

Low Power Electron. Des., pp. 117–22, 2014.

[7] M. J. Dousti, M. Ghasemi-Gol, M. Nazemi, and M. Pedram, “Thermtap: An

online power analyzer and thermal simulator for android devices,” International

Symposium on Low Power Electronics and Design, vol. 18, no. 1, pp. 341–6, 2015.