Design and Optimization of TMO

Design and Optimization of TMO-ReRAM Based Synaptic Devices J.F. Kang1#, B. Gao ... •Many materials have been used to demonstrate ... •Semiconductor-l...

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Design and Optimization of TMOReRAM Based Synaptic Devices J.F. Kang1#, B. Gao1, P. Huang1, Z. Chen1, L.F. Liu1, X.Y. Liu1, S.M. Yu2, H.-S. P. Wong3 #E-mail: 1Institute 2School

3Department

[email protected]

of Microelectronics, Peking University of CIDSE, Arizona State University

of Electrical Engineering, Stanford University

2015 SPICE Workshop

June 29-July 3 2015

Mainz, Germany

1

Outline  Introduction  Physical

Mechanism  Defect Engineering Approach  Optimization of Synapse  Summary

2015 SPICE Workshop

June 29-July 3 2015

Mainz, Germany

2

Outline  Introduction  Physical

Mechanism  Defect Engineering Approach  Optimization of Synapse  Summary

2015 SPICE Workshop

June 29-July 3 2015

Mainz, Germany

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 Introduction Resistive Switching (RS)



• Many materials have been used to demonstrate the reversible bi-stable resistance states (LRS and HRS), which can be switched by voltage, named as resistive

switching (RS) 

RRAM • These RS materials can be used to construct a device, with a typical sandwiched structure, termed as RRAM (Resistive-switching Random Access Memory).

2015 SPICE Workshop

June 29-July 3 2015

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 Introduction 

Two Switching Modes [#] Unipolar

Bipolar

depend on amplitude of applied voltage but not on polarity

depend on the polarity of the applied voltage

[#] H.-S. P. Wong et al., Proc. IEEE, 100, p.1951, 2012. 2015 SPICE Workshop

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 Introduction 

Excellent performances have been demonstrated in transition metal oxide (TMO)-ReRAM [1-6].  Scalability: <10nm devices demonstrated [1-2]

 Compatibility with CMOS using fab-friendly

materials [1-4] • HfO2, TaOx, WOx, Ti, Ta, TiN, NiSi

 Switching speed: <1ns [6]  Switching voltage: <1.5V  Endurance: >1010 cycles [5]

 Read disturb:

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K-S Li et al, VLSI-T2014, C-W. Hsu et al, IEDM2013 W. Chien et al, IEDM2010. X.A. Tran et al, IEDM2011

[5] H.Y. Lee et al, IEDM2010.

 Retention: >10 yrs [6]

>1010

[1] [2] [3] [4]

times [3]

[6] Y.S.Chen et al, IEDM2009

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 Introduction 

Capability to High Density Integration [1,2]

Vertical MOSFET

32/16 Gb Test Chips have been demonstrated [3,4] [1] [2] [3] [4]

H.-Y. Chen, et al., IEDM2012, p.497 (Stanford & PKU); ITRS 2013, http://www.itrs.net, PIDS Chapter T-Y Liu et al, ISSCC2013, p493 (Sandisk & Toshiba) R. Fackenthail, et al, ISSCC2014, p338 (Micron & Sony)

2015 SPICE Workshop

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 Introduction 

New Function Application Concept of RRAM based memristor [1] Memristive switches: both store logic values and perform logic operations [2]

[1] D. B. Strukov et al, Nature 2008, 453, p.80 [2] J. Borghetti, Nature 2010, 464, p.873-876 2015 SPICE Workshop

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 Introduction RRAM based synapses for neuromorphic computing systems [#]

[#] S.M. Yu et al, IEDM2012, p.239 (Stanford and PKU)

Most demonstrated in the bipolar switching mode 2015 SPICE Workshop

June 29-July 3 2015

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 Introduction 

For applications Understand the physical mechnisms of RS Seek technical solutions to construct RRAM devices to achieve targeted performances [1]



In this talk, we will also address Low energy and robust synapse performances of TMO-RRAM [2, 3] Potential for application in a neuromorphic visual system [2] …..

[1] B. Gao, et al. IEEE T-ED, 60(4), pp 1379, 2013; [2] S. Yu, et al. IEDM 2012, p.239; [3] B. Gao, et al, ACS Nano, 8, p. 6998, 2014

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Outline  Introduction  Physical

Mechanism  Defect Engineering Approach  Optimization of Synapse  Summary

2015 SPICE Workshop

June 29-July 3 2015

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 Physical Mechanism 

For the resistive switching (RS) behavior of TMO-RRAM Filament effect has been widely accepted RS is due to the formation and rupture of conducting filaments

R. Waser, Nature. Mat. 2007

However, the physical natures of filaments and the crucial effects to dominate the formation and rupture of filaments are still argued • Conducting filament (CF) type: Vo or metallic ions? • Dominant effect for SET/RESET: G-R or S/D? Thermal or E-field? • Mechanisms of unipolar and bipolar: Same or not? 2015 SPICE Workshop

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 Physical Mechanism 

A Unified Physical Mechanism [1,2]  To clarify fundamental properties of resistive switching behaviors in TMO-ReRAM [1] N. Xu et al, VLSI-T 2008, p.100 [2] B. Gao et al, IEDM2011, p.417



The mechanism is based on filament effect on RS [3] [3] R. Waser, Nature. Mat. 2007

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Unified Physical Mechanism The unified physical mechanism is proposed to clarify these argued issues: Microscopic physical properties correlated with resistive switching in TMO-based RRAM (including unipolar and bipolar)  To explain various resistive switching

characteristics observed in TMO-RRAM  To predict performances of TMO-RRAM

2015 SPICE Workshop

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Unified Physical Mechanism Schematic microscopic properties of RS in TMO-RRAM (B. Gao et al, IEDM2011, p.417) Oxygen

Oxygen vacancy

1. Filament: A percolation path consisting of VO defects 2. Formation and rupture of filaments are correlated with generation and recombination of VO 3. Forming/SET: Generation of new Vo defects and O2- ions induced by Efield and thermal effects in rupture region  Vo defects may be in different states:  Filled state (VO) with 2 electrons in Vo  Unfilled state (Vo2+) w/o electron in Vo

2015 SPICE Workshop

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Unified Physical Mechanism Schematic microscopic properties of RS in TMO-RRAM 4. RESET: Recombination among charged V02+ and O25. Two essential conditions for RESET 1) Occurrence of V02+ states induced by a critical E-field 2) Presence of moveable O2Formation of the state V02+ in the filament at a critical E-field  significant capture section  stable recombination state (LO) 2015 SPICE Workshop

June 29-July 3 2015

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Unified Physical Mechanism 6. Conduction Properties: due to electron transport along Vo filaments Hopping conduction

• Semiconductor-like: Vo are separated from each other • Metallic-like: Vo are closed each other in the clustered

Metallic conduction

2015 SPICE Workshop

• First principle calculations support this opinion

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Outline  Introduction  Physical

Mechanism  Defect Engineering Approach  Optimization of Synapse  Summary

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The resistive switching characteristics are correlated with geometry of Vo filament generation, recombination, and distributions of Vo



It is crucial to control Vo distributions and filament geometry to achieve targeted performances

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 Defect Engineering Approach According to crystal defect theory, the generation and recombination probability of Vo is governed by 𝜸𝑬𝒍𝒐𝒄 − 𝜺𝒂 Eloc: Local electric field 𝒑 = 𝒆𝒙𝒑⁡( ) 𝒌𝑻  A Defect Engineering Approach is proposed [*] 

a: Formation energy of Vo

[*] B. Gao et al, IEEE Tran. ED,.Vol 60, p.1379, 2013 2015 SPICE Workshop

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Defect Engineering Approach

A Defect Engineering Approach is proposed A. Material-Oriented Cell Design

B. Innovation Operation Scheme 2015 SPICE Workshop

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 Defect Engineering Approach

A. Material-Oriented Cell Design Calculated formation energy a of Vo [1, 2] Undoped (eV)

Ti (eV)

Al (eV)

La (eV)

Ga (eV)

HfO2

6.53/6.40a

6.48

4.09

3.42



ZrO2

6.37/6.09b

6.11

3.66

3.74

3.77

a) A. S. Foster et al. PRB 65, 174117(2002) ; b) A. S. Foster et al. PRB 64, 224108(2001) ; c) T. R. Paudel et al. PRB 77, 205202(2008)

Trivalent La or Al doping could effectively reduce a [1] H.W. Zhang et al, APL 96, 2010 2015 SPICE Workshop

[2] B. Gao et al, VLSI2009

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 Defect Engineering Approach

A. Material-Oriented Cell Design 

In the resistive switching (RS) layers of Al- or Ladoped HfO2 or ZrO2 [1-2] Vo are preferentially generated near the trivalent Al or La sites Filaments are preferentially formed along the dopant sites Better controllability of resistive switching could be achieved by using proper doping approaches

[1] H.W. Zhang et al, APL 96, 2010 2015 SPICE Workshop

[2] B. Gao et al, VLSI2009

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 Defect Engineering Approach

A. Material-Oriented Cell Design: Doping Effect

Vo distributions and CFs are full-randomly 2015 SPICE Workshop

Vo and CFs are formed near the dopant sites June 29-July 3 2015

Mainz, Germany

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 Defect Engineering Approach

99.5 98 90 Undoped 70 50 Doped 30 10 LRS

2 0.5

10

2

3

HRS 4

10 10 Resistance ()

10

5

Cumulative Probability (%)

Cumulative Probability (%)

Improved Uniformity by proper doping 99.5 98

Undoped Doped

90 70 50 30 10 2 0.5

Set

Reset -2

-1

0

1

2

Voltage (V)

Expected uniformity improvement is identified by experiments 2015 SPICE Workshop

June 29-July 3 2015

Mainz, Germany

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 Defect Engineering Approach Gradual transitions both in SET and RESET -3

Sharp

10 10 10

Current (A)

Current (A)

10

-4

Set Process

-5

Gradual

Undoped Doped

-6

0.0

0.4

0.8

1.2

1.6

10

-3

10

-4

10

-5

10

-6

10

-7

10

-8

-3.0V -2.5V -2.0V -1.5V

-3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0

Voltage (V)

Voltage (V)

• Better controllability on RS processes achieved in doped HfOx devices • This is beneficial for RRAM as a synapse 2015 SPICE Workshop

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 Defect Engineering Approach

B. Innovation Operation Scheme • Vo density is dependent on local electric field and switching time • Operation schemes (switching time and local electric field) can be used to control Vo distributions Different operation schemes can be expected to achieve different response characteristics!! 2015 SPICE Workshop

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 Defect Engineering Approach

B. Innovation Operation Scheme

Non linear resistance change as a function of pulses is observed when short pulses are applied. 2015 SPICE Workshop

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 Defect Engineering Approach B. Innovation operation scheme

Nearly linear resistance change with pulses is realized when wider pulses are applied. 2015 SPICE Workshop

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Outline  Introduction  Physical

Mechanism  Defect Engineering Approach  Optimization of Synapse  Summary

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TMO-based Synaptic Devices 

Neuromorphic Visual Systems A great amounts of synapses are needed A typical CMOS-based binary synapse consisted of a 8T-SRAM cell [*] New synapse is needed

[*] SRAM based CMOS hardware (IBM, CICC 2011) 2015 SPICE Workshop

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TMO-based Synaptic Devices  TMO-RRAM-based synapse is promising

Analogy between biological Analogy between biological and and artificial RRAM synapse. RRAM based neural networks. 2015 SPICE Workshop

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Optimization of Synapse

Artificial Visual System-based on RRAM and Winner-Take-All algorithm is constructed Integrated-andfire neuron circuit

1st layer: 32× 32 neurons; 2nd layer: 4× 4 neurons between 1st layer and 2nd layer: 16348 RRAM synapses 2015 SPICE Workshop

June 29-July 3 2015

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Synaptic Device Behavior Multi-level resistance states and ultra-low spike energy <1pJ are demonstrated [#] [#] S. Yu, et al, IEDM2012, p.239

Dependence of energy/spike on initial R for training process 2015 SPICE Workshop

Measured and fitted training process with pulse amplitudes.

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Model of TMO-based Synapse A model is developed for the training process of TMO-RRAM synapse

• Resistance variation effect during training process • Model parameters can extracted from measured data. 2015 SPICE Workshop

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Mainz, Germany

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TMO-based Synaptic Devices Resistance Evolution under 400 RESET pulses • In low resistance regime, fluctuation is smaller but suffers from high spike energy • In high resistance regime, low spike energy but larger fluctuation presented Larger fluctuation or variation may cause degradation of recognition accuracy of the neuromorphic systems 2015 SPICE Workshop

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Simulated System Performances Training Images and Initial Conductance Map

2D Gaussian bar: Random center and random orientation

Before the training randomized around 20kΩ 2015 SPICE Workshop

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Simulated System Performances Resistance Diverges and Orientation Map Emerges During the training

After the training 2015 SPICE Workshop

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Optimized Synaptic Devices Can we realize synaptic performances with both low spike energy and high recognition accuracy ? ?

Geometric mean of more than 2 devices in parallel can significantly suppress the impact of intrinsic fluctuation effect 2015 SPICE Workshop

June 29-July 3 2015

Mainz, Germany

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Optimized Synaptic Devices Optimized architecture of a neuromorphic system using robust synapse is proposed • A 1D1R synaptic cell is introduced • 1D is applied to perform logarithm function on the device resistance Geometric mean calculation on resistance is replaced by the logarithm function. 2015 SPICE Workshop

June 29-July 3 2015

Mainz, Germany

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Simulated System Accuracy • Single RRAM device • Geometric mean of two devices • Two parallel 1D-1R cells

• Significant improvement on recognition accuracy is achieved by the architecture of 2 parallel 1D1R. • Array integration approach is a great challenge 2015 SPICE Workshop

June 29-July 3 2015

Mainz, Germany

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Optimized Synaptic Devices 

3D vertical ReRAM array architecture as synapses Easily to achieve high density of integration Significantly to immunize resistance variation during training process of synapses

A synapse: devices in the same pillar electrode 2015 SPICE Workshop

June 29-July 3 2015

Mainz, Germany

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3D Vertical RRAM Arrays 

Measured training process of top and bottom ReRAM devices in the 3D vertical array 2 layered devices are fabricated Nearly constant device performance both in top and bottom layers is measured. Significantly improved accuracy achieved. 2015 SPICE Workshop

June 29-July 3 2015

Mainz, Germany

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TMO-based Synaptic Devices 

Measured training process for the 3D vertical synaptic devices Different initial R states can be achieved by different current compliances Initial R is set to ~1MΩ, maximum energy consumption per spike <1 pJ. 2015 SPICE Workshop

B. Gao et al, ACS Nano 8, 6998, 2014

June 29-July 3 2015

Mainz, Germany

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Outline  Introduction  Physical

Mechanism  Defect Engineering Approach  Optimization of Synapse  Summary

2015 SPICE Workshop

June 29-July 3 2015

Mainz, Germany

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Summary A unified physical mechanism is proposed to elucidate the resistive switching of TMORRAM  A defect engineering approach is developed to design and optimize RRAM performances 



Excellent controllability on RS behaviors is demonstrated in optimized RRAM devices based on the defect engineering approach.

2015 SPICE Workshop

June 29-July 3 2015

Mainz, Germany

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Summary 

Multi-level resistance states are realized in the optimized RRAM



Robust synaptic behaviors with sub-pJ energy per spike are realized in the optimized RRAM



Optimized architectures of TMO-RRAM synapse are proposed to improve system performances. 2015 SPICE Workshop

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Mainz, Germany

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