Experimental and theoretical understanding of Forming, SET and RESET operations in Conductive Bridge RAM (CBRAM) for memory stack optimization

Transcription

1 Experimental and theoretical understanding of Forming, SET and RESET operations in Conductive Bridge RAM (CBRAM) for memory stack optimization J. Guy, G. Molas, P. Blaise, C. Carabasse, M. Bernard, A. Roule, G. Le Carval, V. Sousa, H. Grampeix, V. Delaye, A. Toffoli, J. Cluzel, P. Brianceau, O. Pollet, V. Balan, S. Barraud, O. Cueto, G. Ghibaudo, F. Clermidy, B. De Salvo, L. Perniola CEA, LETI, MINATEC Campus, GRENOBLE France

2 Oxide Filament Conductive Bridge Memories (CBRAM) Electrochemical reversible formation / dissolution of a metallic filament in a solid electrolyte matrix Envisaged for Flash replacement: Simple structure (2 terminals) Ease of integration in the BEOL Low power consumption Cu Plug 2

3 Aim of this Work Oxides have been introduced as electrolyte to enhance the thermal stability But oxide CBRAMs require Forming Physical understanding (Forming, SET, RESET) is crucial to the CBRAM industrialization Based on experimental results and simulations Monte Carlo simulation of SET and Forming Physical interpretation of materials properties involved in the switching mechanisms Optimization of CBRAM stack to improve memory performances 3

4 Outline Aim of this work Model description Experimental Results SET and Forming RESET Conclusion 4

5 Outline Aim of this work Model description Experimental Results SET and Forming RESET Conclusion 5

6 Model Description Initialization of the Grid time = 0 Solve the Poisson Equation Calculation of the event rates : Selection of the iteration time Event probabilities calculation Occurring event determination Particules location update Solving the percolation model : I Cell The model is based on a Monte Carlo approach. The model computes the oxidation, diffusion and reduction of the active metal during Forming and SET. time = time + I cell > I SET? No Yes The cell is switched, t Switching = time 6

7 Model Description Copper atoms Inert metal atom Top Electrode Initialization of the Grid time = 0 Solve the Poisson Equation Calculation of the event rates : Γ Selection of the iteration time Δt = ln rand Event probabilities calculation p = 1 exp Γ. t Γ Resistive Layer Bottom Electrode Each dot of the Grid corresponds to a possible position of the Cu ions Time = 0 s 7

8 Model Description Initialization of the Grid time = 0 Solve the Poisson Equation Calculation of the event rates : Γ Selection of the iteration time Δt = ln rand Γ The electric field is the driving force of the switching mechanisms. Objective of the step : Calculating the electric field on every points of the Grid Event probabilities calculation p = 1 exp Γ. t Occurring event determination 8

9 Model Description Initialization of the Grid time = 0 Solve the Poisson Equation Calculation of the event rates : Γ The event rate (s -1 ) represents the average time required for a event to happen. TE Selection of the iteration time Δt = ln rand Event probabilities calculation p = 1 exp Γ. t Occurring event determination Particules location update Γ Inert metal atom Copper ions Copper atoms Volume diffusion BE Surface diffusion Surface desorbtion Oxidation Reduction 9

10 Energy Model Description Initialization of the Grid time = 0 Two kinds of event: diffusions and reactions, both following the same behavior: Solve the Poisson Equation Calculation of the event rates : Γ Selection of the iteration time Δt = ln rand Event probabilities calculation p = 1 exp Γ. t Occurring event determination Γ E A Distance With : E A : energy barrier height q: charges number d: distance between 2 Cu sites ε: electric field χ: electric susceptibility ΔE (1 2) : energy difference of the two states Particules location update 10

11 Energy Model Description Initialization of the Grid time = 0 Solve the Poisson Equation Two kinds of event: diffusions and reactions, both following the same behavior: E A qd χ 3 ε 2 ΔE Calculation of the event rates : Γ Selection of the iteration time Δt = ln rand Event probabilities calculation p = 1 exp Γ. t Occurring event determination Γ qv dhopping Distance With : E A : energy barrier height q: charges number d: distance between 2 Cu sites ε: electric field χ: electric susceptibility ΔE (1 2) : energy difference of the two states Particules location update 11

12 Model Description Initialization of the Grid time = 0 Two kind of events: diffusion and reaction, both following the same behavior: Solve the Poisson Equation Calculation of the event rates : Γ Selection of the iteration time Δt = ln rand Event probabilities calculation p = 1 exp Γ. t Occurring event determination Particules location update Γ Diffusion: Γ = ν. exp Reaction: Γ = ν. exp With: ν: vibration frequency (10 13 s -1 ) q: charges number d M M : Hoppig distance ε: electric field E A qd M M χ 3 ε k B T x, y E A ± 1 2 qd χ M M 3 ε + φ RL φ M k B T χ: electric susceptibility φ M/RL : Metal and RL work function k B : Boltzmann Constant T: Temperature. 12

13 Model Description Initialization of the Grid time = 0 Solve the Poisson Equation Calculation of the event rates : Γ Selection of the iteration time Δt = ln rand Event probabilities calculation p = 1 exp Γ. t Occurring event determination Γ To save computation time, the iteration duration depends on the events rates and is updated at each iteration. The rand value corresponds to the probability (between 0 and 1) that none of the events occurs. Particules location update Solving the percolation model : I Cell 13

14 time = 0 Solve the Poisson Equation Model Description Calculation of the event rates : Γ Selection of the iteration time Δt = ln rand Γ Event probabilities calculation p = 1 exp Γ. t Occurring event determination Particles location update Each event probability is calculated and the occurring event is randomly determined between all the probabilities normalized. Solving the percolation model : I Cell time = time + Δt I cell > I SET? No 14

15 Calculation of the event rates : Γ Selection of the iteration Model Description ln rand time Δt = Γ Event probabilities calculation p = 1 exp Γ. t Occurring event determination Particles location update Solving the percolation model : I Cell time = time + Δt I cell > I SET? Yes No The cell is switched, t Switching = time Knowing the occurring event, the particles locations is updated and the current calculated following a percolation model : Current path Resistance Hopping site: Cu-occupied or free 15

16 p = 1 exp Γ. t Model Description Occurring event determination Particles location update Solving the percolation model : I Cell time = time + Δt I cell > I SET? Yes No The cell is switched, t Switching = time Once the current is calculated, the time is updated and the model verify the current level flowing in the cell. Depending the current level, another iteration starts or the switching time and voltage are extracted. 16

17 Current (A) Model Description Running simulation Initialization of the Grid time = 0 Solve the Poisson Equation TE 1 frame / 30 iterations Calculation of the event rates : Selection of the iteration time RL Event probabilities calculation Occurring event determination Particules location update BE V FORMING Solving the percolation model : I Cell time = time No I cell > I SET? Yes The cell is switched, t Switching = time Voltage (V) 17

18 Current (A) Model Description Running simulation Initialization of the Grid time = 0 Solve the Poisson Equation TE 1 frame / 30 iterations Calculation of the event rates : Selection of the iteration time RL Event probabilities calculation Occurring event determination Particules location update BE V FORMING Solving the percolation model : I Cell time = time No I cell > I SET? Yes The cell is switched, t Switching = time Voltage (V) 18

19 Outline Aim of this work Model description Experimental Results SET and Forming RESET Conclusion 19

20 Samples Description 1R nano-trench structures [Guy IEDM 13] Memory stack: METAL Bottom electrode (down to 5nm liner) Nano-Trench (down to 50nm defined by e-beam) Trench cross section² ALD/PVD Oxide electrolyte 20nm PVD Cu based Top Electrode 5x50nm² Cell Size Liner cross section Cu based TE Down to 5 nm Oxide RL Down to 50nm Cu based TE Oxide RL BE BE 20

21 Samples Description 1R nano-trench structures [Guy IEDM 13] Memory stack: METAL Bottom electrode (down to 5nm liner) Nano-Trench (down to 50nm defined by e-beam) ALD/PVD Oxide electrolyte 20nm PVD Cu based Top Electrode 5x50nm² Cell Size 21

22 Experimental Results Study based on various materials combinations and thicknesses (see tables) The study has been conducted following two trends, first the Forming and SET and then the RESET. The goal is to determine the impact of the CBRAM stack on Forming, SET and RESET. Samples S1 S2 S3 S4 S5 S6 S7 S8 S9 BE TiN TiN WSi TiN TiN WSi Ta Ta Ta RL Al 2 O 3 3.5nm Al 2 O 3 3.5nm Al 2 O 3 3.5nm Al 2 O 3 5nm MO x 5nm Al 2 O 3 5nm MO x 5nm HfO 2 5nm HfO 2 1nm MO x 5nm TE Cu CuTe x CuTe x CuTe x CuTe x CuTe x CuTe x CuTe x CuTe x

23 t FORMING (s) Top Electrode impact on SET and Forming Data: CuTe x Data: Cu Simulations TE RL V pulse (V) BE Both TE based on copper alloy with different compositions ( different Work Function ). Increasing the Work Function leads to a decrease of V forming. 23

24 t FORMING (s) Top Electrode impact on SET and Forming Data: CuTe x Data: Cu Simulations F TE e - F RLCu 10-4 E A V pulse (V) TE Work Function is tied to the final e - energy level at the end of oxidation reaction Increasing the Work Function reduces E A of the reaction and eases the oxidation reaction 24

25 t FORMING (s) Top Electrode impact on SET and Forming Data: CuTe x Data: Cu Simulations F TE e - F RLCu 10-4 E A V pulse (V) TE Work Function is tied to the final e - energy level at the end of oxidation reaction Increasing the Work Function reduces E A of the reaction and eases the oxidation reaction 25

26 t FORMING (s) Bottom Electrode impact on SET and Forming Data: WSi Data: TiN Simulations V pulse (V) BE with different Work Function. TE RL BE Increasing the Work Function leads to a decrease of V forming. 26

27 t FORMING (s) Bottom Electrode impact on SET and Forming Data: WSi Data: TiN Simulations V pulse (V) F TE V TE Cu BE F BE Increasing the BE Work Function leads to an increase of the over potential between the electrodes. Higher electrical potential leads to higher electric field leading to higher reaction and diffusion speeds. 27

28 t FORMING (s) Bottom Electrode impact on SET and Forming Data: WSi Data: TiN Simulations V pulse (V) F TE Increasing the BE Work Function leads to an increase of the over potential between the electrodes. Higher electrical potential leads to higher electric field leading to higher reaction and diffusion speeds. V TE Cu BE F BE 28

29 t FORMING (s) Resistive Layer impact on SET and Forming nm 3.5 nm Simulations TE RL V pulse (V) Two different RL thicknesses. BE Increasing the RL thicknesses leads to an increase of V forming. 29

30 t FORMING (s) Resistive Layer impact on SET and Forming nm 3.5 nm Simulations V pulse (V) V TE Cu BE F BE Increasing the RL thickness leads to a decrease of the electric field between the electrodes. Lower electric field leads to lower reaction and diffusion speeds. 30

31 t FORMING (s) Resistive Layer impact on SET and Forming nm 3.5 nm Simulations V pulse (V) Increasing the RL thickness leads to a decrease of the electric field between the electrodes. Lower electric field leads to lower reaction and diffusion speeds. V TE Cu BE F BE 31

32 t FORMING (s) Resistive Layer impact on SET and Forming Data: Al 2 O 3 Data: MO x Simulations V pulse (V) TE RL BE Several variable parameters regarding RL impact. Decreasing the Work Function with fixed other parameters decreasing the V Switching. 32

33 t FORMING (s) Resistive Layer impact on SET and Forming Data: Al 2 O 3 Data: MO x Simulations F TE E A e - F RL Cu V pulse (V) Decreasing the RL Work function leads to a decrease of the energy barrier of the oxidation. Lower energy barrier leads to higher reaction speeds and lower V Switching 33

34 t FORMING (s) Resistive Layer impact on SET and Forming Data: Al 2 O 3 Data: MO x Simulations F TE e - F RLCu 10-6 E A V pulse (V) Decreasing the RL Work function leads to a decrease of the energy barrier of the oxidation. Lower energy barrier leads to higher reaction speeds and lower V Switching 34

35 Resistive Layer impact on SET and Forming t FORMING (s) Data: Al 2 O 3 Data: MO x Simulations V pulse (V) Low V Forming can be achieved with high permittivity oxides. By tuning the hopping distance (density ) it is possible the further reduce the V Forming. Cu-Cu d Hopping (Å) Al 2 O 3 High-k TE RL BE RL Electrical Permittivity V FORMING (V)

36 t FORMING (s) Resistive Layer impact on SET and Forming Data: Al 2 O 3 Data: MO x Simulations Event rates : Γ = ν. exp E A qd M M χ 3 ε k B T x, y 10-6 With χ = ε r V pulse (V) Lowering the hopping distance (density, preferential sites ) leads to better time-voltage tradeoff High permittivity oxides also induce a better timevoltage tradeoff 36

37 t FORMING (s) SET - Conclusion It is possible to optimize the Forming and SET characteristics in two ways : Shifting the V Switching and the time-voltage curve (by tuning energy levels and F) Changing the slope of the time-voltage curve (by tuning the parameters modifying the field efficiency) F TE F RL e RL d Cu-Cu F TE 4.4 ev Step 0.2 ev 5.6 ev Faster Forming speed V pulse (V) Al 2 O 3 HfO 2 Faster Forming speed F RL V pulse (V) 0.6 ev Step 0.2 ev 2 ev Al 2 O 3 HfO 2 Better disturb immunity ε RL V pulse (V) 3 Step 2 23 Al 2 O 3 HfO 2 Better disturb immunity d Hopping V pulse (V) 1.8 Å Step 0.2 Å 3.8 Å

38 Outline Aim of this work Model description Experimental Results SET and Forming RESET Conclusion 38

39 Bottom Electrode impact on RESET WSi 100% TiN 80% 60% R OFF increase 40% 20% 0% R OFF (Ω) 100% TiN 80% 60% V RESET increase 40% 20% 0% -1,5-1 -0,5 0 V RESET (V) Using WSi instead of TiN for the BE leads to an increase of R OFF of one decade Howerver WSi also increases the required V RESET WSi 39

40 Temperature (x10 3 K) Temperature (K) Bottom Electrode role on V RESET -1 Higher temperature at fixed V Voltage (V) -0.2 WSi TiN Using COMSOL Multiphysics we can see that the temperature increases faster with TiN BE. BE : TiN k TiN = 30 (W.m -1.K -1 ) BE : Wsi k WSi = 170 (W.m -1.K -1 ) WSi CBRAM requires higher voltage to reach the sufficient energy (voltage and temperature) for the RESET. 40 TE RL BE

41 Temperature (x10 3 K) Bottom Electrode role on V RESET -1 Higher temperature at fixed V Voltage (V) -0.2 WSi TiN 0 WSi 100% TiN 80% 60% 40% 20% 0% -1,5-1 -0,5 0 V RESET (V) Using COMSOL Multiphysics we can see that the temperature increases faster with TiN BE. WSi CBRAM requires higher voltage to reach the sufficient energy (voltage and temperature) for the RESET. 41

42 Temperature (K) Thermal impact of the Bottom Electrode BE : TiN k TiN = 30 (W.m -1.K -1 ) TE RL T MAX = 420 K BE : TiN The position of Highest temperature depends on the thermal conductivity of BE. The higher the BE thermal conductivity the farther from BE the hot spot. 1 nm TE RL T MAX = 860 K BE : WSi Temperature (K) BE : WSi k WSi = 170 (W.m -1.K -1 ) 42

43 TiN Temperature (K) Current Thermal distribution and RESET TE RL T MAX = 420 K BE : TiN Data: TiN Voltage (V) V 7 V 6 V 5 V 4 V 3 V 2 V 1 Erased Area Dissolution 43

44 TiN WSi Temperature (K) Current Thermal distribution and RESET TE RL T MAX = 420 K BE : TiN Data: WSi Data: TiN nm TE RL T MAX = 860 K BE : WSi Temperature (K) Voltage (V) Erased Area Dissolution V 7 V 6 V 5 V 4 V 3 V 2 V 1 Erased Area Dissolution 44

45 TiN WSi Temperature (K) Current Thermal distribution and RESET TE RL T MAX = 420 K BE : TiN k TiN = 30 (W.m -1.K -1 ) Data: WSi Data: TiN Voltage (V) 1 nm TE RL T MAX = 860 K BE : WSi k WSi = 170 (W.m -1.K -1 ) Temperature (K) Erased Area Erased Area V 7 WSi 100% TiN 80% 60% R OFF increase 40% 20% 0% R OFF (Ω) WSi impact on RESET Higher Thermal Conductivity Higher V RESET Increased distance between the T max location and BE Higher R OFF 45

46 RESET - Conclusion The RESET operation is governed by energy brought to the system under the form of voltage and temperature. The thermal dissipation of the bottom electrode plays a great role in V RESET and R OFF. By delaying the elevation of the temperature and thus the attainment of the RESET energy. By modifying the position of the highest energy point: breaking point of the filament. 46

47 Outline Aim of this work Model description Experimental Results SET and Forming RESET Conclusion 47

48 Conclusion We integrated and studied CBRAM devices with various BE, TE ad RL. We presented a Monte Carlo simulation based on the physical properties of the CBRAM materials. Forming and SET time/voltage characteristics can be modified in term of slope and position: The work functions shift the characteristics Cu-Cu distance and dielectric permittivity changes the slope of the characteristic We also showed the great importance of a high BE thermal conduction to improve the R OFF. 48

49 Thank you for your attention