TCAD Simulation for ePCM Technology Development · TCAD ePCM Modeling Technology Needs 7 Why ePCM simulation is needed? Predictable info in order to simplify process trials and integration

TCAD Simulation for ePCM

Technology Development

Elisa Petroni

Advanced Simulation for NVM, LETI Innovation Days – Grenoble

28/06/2019

Outline

• Introduction

• embedded PCM for automotive applicationsMaterials and architecture optimization

• TCAD device modelingtechnology needs and simulation calibration

• TCAD test casepredictivity and architecture optimum assessment

• Different approaches & outlooks

• Conclusions

2

IntroductionST Product Family Focus

3

The leading provider of products and solutions for Smart Mobility and the Internet of Things

Portfolio delivering complementarity for target end markets, and

synergies in R&D and manufacturing

Dedicated

Automotive ICs

Discrete &

Power

Transistors

Digital

ASICs

MEMS &

Specialized

Imaging Sensors

Analog, Industrial &

Power Conversion

ICs

General Purpose &

Secure MCUs

EEPROM

IntroductionPCM Working Principles

4

Phase Change Materials:

High r logic 0 Low r logic 1

Amorphous Crystalline

SET pulse

RESET pulse

time, nsec

Te

mp

era

ture

, °C

Tmelt

Tc

• Semiconductor ternary alloy (GST), usually Ge2Sb2Te5

• Two (or more) stable phases @ Tamb amorphous vs. crystalline phases

• Reading mechanism resistance change of the GST

• Writing mechanism self-heating due to current flow (Joule effect)

GST 225 22% Ge 22% Sb 55% Te

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.1

.1

.1

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Sb

Ge Te

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ePCM for AutomotiveWhy and How

5

embedded-PCM for automotive:

• Easy integration with advanced logic

• Low voltage

• No impact on CMOS front-end

• Cost-effective

• Few additional masks

• Versatility & Scalability

• Unipolar programming MOS or BJT selector depending on specific

application requirements

• Programming current scaling with cell area reduction

• Reliability

• Extrinsic reliability assessed both after soldering and for 10y @150C HTDR

ePCM cell

ePCM for AutomotiveMaterial Engineering

6

Ge-rich chalcogenide:

• Germanium content is the key to improve Tc

of Ge-Sb-Te compounds

• Tc ~ 350 °C compliant with temperature

soldering profile for automotive

TCAD ePCM ModelingTechnology Needs

7

Why ePCM simulation is needed?

Predictable info in order to simplify process trials and integration speeding up technology development!

• Scalability efficiency check for dimension scaling (isotropic, non-

isotropic, etc…)

• Cell architecture functionality of different cell geometries and working

concepts

• Material properties optimization process improvement of

surrounding steps (i.e. for thermal confinement increasing …)

Russo et al., IEEE Trans. Electron Devices, 55, 2 (2008)

Synopsys Inc., Application Notes (2008)

8TCAD ePCM Modeling

Transition Rates (1/2)Phase Transition Modeling:

TCAD approaches differ for selected phase transition model

Transition Rate Equations:

SDevice by Synopsys® (→ Poisson-continuity solver)

• Each phase i is represented by volume fraction σi si = 1

• Temporal variations of si are defined by ordinary differential

equations ሶsi = σj≠iσtϵTijcij sj − eijsi

• Initial guess for si from equilibrium conditions energy, temperature

capture transition rate = c

emission transition rate = e

Tmelting = 888 K

crystalline

amorphous

Zi = giexp(−βEi)

Two phase model crystalline vs. noncrystalline

Melted state amorphous state with

physical properties for

T > Tm = melted state properties

TCAD ePCM ModelingTransition Rates (2/2)

9

Transition rate:

definition of one transition rate, tipically cA Ccapture rate c

Tg

For emission transition rates detailed balance principle:

e

c=sj∗

si∗

C A

• Arrhenius law

• Peng growth

• Simplified rate (accounting remaining crystallization

process from Tg to Tm) Theta function

TCAD ePCM ModelingCell Structure

10

3D structure

Wall architecture

• optimized heater dimensions enhancing joule

heating effect and programming efficiency

• Self-aligned structure TE + GST + Heater

Heater

Bottom electrode

Top electrode

Bottom electrode

Top electrode

c-GST

Y view direction X view direction

M0 CD

TCAD ePCM ModelingEmulated Cell

11

Calibration flow:

contact (BL)

conta

ct

(GN

D)

2D slice

std metals and insulators (ρ, kth, cV …):

surrounding materials, fundamental in ePCM

ACTIVE CELL:

RCELL = RGST + Rheater + RTE

RGST

RTE

Rheater

TE

BE

GST

Heater

Heater & TE – metals (ρ, kth, cV …)

GST semiconductor! Dirichlet boundary conditions

TCAD ePCM ModelingGST Material

12

GST is modeled as p-doped semiconductor

linking RGST to microscopic and transport properties

RGST = ρGST ∙ geometrical factor

In a semiconductor:

ρGST = ρ∞ eEACTkBT

ρGST =1

q(nμn + pμp)

ρGST ~1

qpμp=

1

qNVDOSμp

eEACTkBT

𝛍 calibration

Crystalline Amorphous = Melted

μn 2 150

μp 425 ൝150 at T = 300Kμpcrys

at T = 650K

𝐄𝐀𝐂𝐓 calibration

by tuning band energy structure in the two phases

(values to be considered as “effective”, that

take account of experimental 𝛒𝐆𝐒𝐓 = ρ∞)

TCAD ePCM ModelingBand Structure

13

from Adler (1978):

𝐄𝐆𝐀𝐏 = 0.5 eV → 0.715 eV

𝐄𝐓𝐑𝐀𝐏(−)

= 0.09 eV

Both crystalline and amorphous GST result in

p-doped semiconductors

EACT results consistent with experimental data

TCAD ePCM ModelingGST Calibration

14

• experimental data

simulated data

𝐑𝐒𝐄𝐓 = 𝐑∞ 𝐞𝐄𝐀𝐂𝐓𝐤𝐁𝐓

Right calibration of

ρGST = ρ∞ eEACTkBT

… but using “effective” or arbitrarily fixed values!

Experimental measurements are needed for

realistic ρGST calibration:

1. Hall measurements for μ and NVDOS

determination

2. Ellipsometry measurements for EGAP

estimation

TCAD ePCM ModelingSET State

15

SET characteristic:

• experimental data

• simulated data

RSET

RON

RGST

RTE

Rheater

for

𝐓𝐂𝐄𝐋𝐋 > 𝐓𝐦𝐞𝐥𝐭

RGST ≪ Rheater + RTE

→ RCELL = 𝐑𝐡𝐞𝐚𝐭𝐞𝐫 + 𝐑𝐓𝐄 = 𝐑𝐎𝐍

TCAD ePCM Test CasePredictivity

16

Scalability:

WCELL1

WCELL2

WCELL3

WCELL4

• experimental data

• simulated data

Sensitivity:

WCELL (= M0 CD)

• experimental data

• simulated data

Hheater

Hheater 3

Hheater 2

Hheater 1

WCELL1 > WCELL2 > WCELL3 > WCELL4Hheater1 > Hheater2 > Hheater3

TCAD ePCM Test CaseSET Sensitivity

17

RON sensitivity: RSET sensitivity:

Hheater

HGST

RON ∝ Hheater

𝐑𝐎𝐍 ~ 𝐑𝐡𝐞𝐚𝐭𝐞𝐫 + 𝐑𝐓𝐄

→ less influenced by HGST

RSET ∝ Hheater

𝐑𝐒𝐄𝐓 𝐯𝐬. 𝐇𝐆𝐒𝐓

→ analytical formula?

• vs. Hheater

• vs. HGST

• vs. Hheater

• vs. HGST

18

RGST = ρGST ∙ 𝐠𝐞𝐨𝐦𝐞𝐭𝐫𝐢𝐜𝐚𝐥 𝐟𝐚𝐜𝐭𝐨𝐫RGST = ρGSTන

0

HGST dr

S(r)

RGST = ρGSTන0

HGST dr

WCELL(t + 2αr)

RGST = ρGSTln(1 +

2αHGSTt )

2αWCELL𝛂

𝟐𝛂𝐫 + 𝐭

𝐭

𝐫

𝐇𝐆𝐒𝐓

TCAD ePCM Test CaseRSET Sensitivity

α = α(V,WCELL, 𝐇𝐆𝐒𝐓)

second order effects

TCAD ePCM Test CaseImelting Optimization

19

Optimizing cell architecture in order to minimize melting current (𝐈𝐦𝐞𝐥𝐭𝐢𝐧𝐠)

888 K

𝐈𝐦𝐞𝐥𝐭𝐢𝐧𝐠

for T > Tmelting = 888 K

→ melted GST

for present range of RGST

→ Heater Heating regime

𝐈𝐦𝐞𝐥𝐭𝐢𝐧𝐠 ∝ ൗ𝟏 𝐑𝐡𝐞𝐚𝐭𝐞𝐫

vs.

𝐑𝐒𝐄𝐓 ∝ 𝐑𝐡𝐞𝐚𝐭𝐞𝐫

Lowering Imelting

decreases readout

performance

TRADE OFF is needed

T = Tamb + I2RheaterRheaterth

TCAD ePCM Test CaseGeometrical Optimum

20

𝐈𝐦𝐞𝐥𝐭𝐢𝐧𝐠 @ fixed 𝐑𝐒𝐄𝐓: when Hheater is increased, HGST is decreased and vice versa

𝐑𝐒𝐄𝐓𝟏

𝐑𝐒𝐄𝐓𝟐𝐑𝐒𝐄𝐓𝟑

𝐑𝐒𝐄𝐓𝟒

minimum of Imelting varying cell geometry

Slight increase Hheater/HGST ratio on RSET3

RSET1 < RSET2 < RSET3 < RSET4

ST CELL

increasing Hheater/HGST ratio

geometrical optimum

Trade off between heating efficiency and

hot spot position

c𝜕T

𝜕t− 𝛻 ∙ (kth𝛻T) ∝ Jn,p

Different TCAD Approaches & Outlooks 21

Beyond Transition Rate Modeling …

… other TCAD approaches:

• Phase Field Method → dynamic of transition parameter and free energy

• Monte Carlo techniques → randomness of nucleation process

… Atomistic & molecular dynamics simulations

→ speeding up material engineering and understanding A. Glière et al., Proc. SISPAD Conference, 63 (2011)

G. Novielli et al., IEDM Technical Digest, 589 (2013)

Long-term Outlook:

coupled system of TCAD and ab-initio simulations …

… in an industrial environment!

Ge TeSb

FORMING EFFECT

Conclusions 22

• PCM solution is well positioned to match specific eNVM target

• TCAD simulation is a powerful tool yet for obtaining predictable info in terms of geometrical

scaling & sensitivity

• Correct calibration of phase change material requires deep knowledge of phase transition

physics and experimental measurements of energetic and transport properties

• Developed GST modeling is able to predict scaling efficiency and sensitivity of SET

resistivity

• Thanks to TCAD simulation, we are able to assess the geometrical optimum of our

technology, very close to present ST working point

Acknowledgments 23

ePCM team(ST Agrate)

Massimo Borghi

Elisabetta Palumbo

Octavian Melnic

Alessandro Motta

Paola Zuliani

Roberto Annunziata

For support in

TCAD model development(ST Agrate)

Lucia Zullino(ST Rousset)

Roberto Simola(ST Crolles)

Thomas Cabout

Thank you!