Investigation of Forming, SET, and Data Retention of ... - IEEE Xplore

3482

IEEE TRANSACTIONS ON ELECTRON DEVICES, VOL. 62, NO. 11, NOVEMBER 2015

Investigation of Forming, SET, and Data Retention of Conductive-Bridge Random-Access Memory for Stack Optimization Jérémy Guy, Gabriel Molas, Philippe Blaise, Mathieu Bernard, Anne Roule, Gilles Le Carval, Vincent Delaye, Alain Toffoli, Gérard Ghibaudo, Fellow, IEEE, Fabien Clermidy, Barbara De Salvo, and Luca Perniola

Abstract— In this paper, we investigate in depth Forming, SET, and Retention of conductive-bridge random-access memory (CBRAM). A kinetic Monte Carlo model of the CBRAM has been developed considering ionic hopping and chemical reaction dynamics. Based on inputs from ab ini t i o calculations and the physical properties of the materials, the model offers the simulation of both the Forming/SET and the Data Retention operations. It aims to create a bond between the physics at atomic level and the device behavior. From the model and experimental results obtained on decananometric devices, we propose an understanding of the physical mechanisms involved in the CBRAM operations. Using the consistent Forming/SET and Data Retention model, we obtained good agreement with the experimental data. Finally, the impact of each layer of the CBRAM on the Forming/SET behavior is decorrelated, allowing an optimization of the performance. Index Terms— Conductive-bridge random-access memory (CBRAM), Data Retention, Forming, kinetic Monte Carlo (KMC) modeling, resistive memory, resistive RAM, SET, transition-state theory (TST).

I. I NTRODUCTION

C

ONDUCTIVE-BRIDGE random-access memory (CBRAM) is a promising nonvolatile memory technology envisaged as an alternative to flash memory. The strength of this technology resides in its low power consumption, high speed, simple structure, and ease of integration in the back end of a logic process. It has been accepted that this nonvolatile memory is based on the reversible formation and dissolution of a conductive

Manuscript received March 6, 2015; revised August 13, 2015; accepted September 1, 2015. Date of publication September 25, 2015; date of current version October 20, 2015. The review of this paper was arranged by Editor J. S. Suehle. (Corresponding author: Jérémy Guy.) J. Guy, G. Molas, P. Blaise, M. Bernard, A. Roule, G. Le Carval, V. Delaye, A. Toffoli, F. Clermidy, B. De Salvo, and L. Perniola are with Commisariat à l’Énergie Atomique–Laboratoire d’Électronique et de Technologie de l’Information, Grenoble 38054, France (e-mail: jeremy. [email protected]; [email protected]; [email protected]; mathieu.bernard @cea.fr; [email protected]; [email protected]; [email protected]; [email protected]; [email protected]; [email protected]; [email protected]). G. Ghibaudo is with Institut de Microélectronique, Électromagnétisme et Photonique–Laboratoire d’Hyperfréquences et de Caractérisation, Minatec/Grenoble Institute of Technology, Grenoble 38016, France (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TED.2015.2476825

filament (CF) inside a resistive layer (RL) [1], [2]. Various RL materials have been studied from chalcogenides (GeSe and so on [3], [4]) to oxides (Al2 O3 and so on [5], [6]), these latter providing an improved Data Retention behavior. However, the main drawback of oxide-based CBRAM is the necessity of a high-voltage Forming operation. To respond to the electrical needs of this unique Forming cycle, a complex circuitry may be required at the cost of an integration density loss [7]. Thus, the technical knowledge of the CBRAM keeps growing, leading to high-density integration [8], whereas the physical understanding of the mechanisms involved during the SET, RESET, and Retention still has to be clearly understood. Several studies have been carried out in order to investigate these mechanisms, either by focusing on CF as the growing entity at the device scale, based on continuous [9] or discrete phenomena [10], or by performing the first principle calculations at the materials level to get insights into the filament composition [11]. In this paper, we present and describe an extended version of the kinetic Monte Carlo (KMC) simulation introduced in [12]. We propose a consistent model of Forming/SET and Data Retention using a trap-assisted tunneling (TAT) electrical conduction model. This KMC simulation creates a bridge between the physics at the atomic scale and the device behavior and performance. From ab i ni ti o inputs and material physical properties, based on the atomic and electronic migration, we propose a physical interpretation of the mechanisms involved in the switching operation and Retention. Moreover, an experimental study on nanotrench CBRAM structures [6], with various material combinations, has been conducted and coupled to the KMC simulations to extract the impact of each component on the CBRAM characteristics. A specific attention is given to the time– voltage dilemma, investigating the time–voltage dependence of the Forming and SET characteristics. Finally, the material properties and the understanding of the mechanisms involved in the CBRAM operation allowed us to put forward the optimized CBRAM stacks for improved switching speeds. II. T ECHNOLOGICAL D ETAILS The nanotrench CBRAM structure studied in this paper is represented (Fig. 1). The bottom electrode (BE) is defined by a metallic liner deposited by Chemical Vapor

0018-9383 © 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

GUY et al.: INVESTIGATION OF FORMING, SET, AND DATA RETENTION OF CBRAM

Fig. 1. Schematic process flow description of the nanotrench CBRAM and transmission electron microscopy cross sections. TABLE I S TUDIED S AMPLES AND M ATERIAL S TACK VARIATIONS

Deposition in an etched via. The thickness of the layer, down to 5 nm, controls the length of the BE. The via is then filled with SiO2 , and the BE contact is opened by chemical mechanical planarization to create a ring. The second critical dimension of the memory is defined by a nanotrench, etched in a SiN capping layer deposited over the planarized BE. This nanotrench is defined by electron-beam lithography down to 50 nm and leads to the reduction of the CBRAM active area down to 5 × 50 nm2 . A metal oxide acting as an RL (Al2 O3 deposited by ALD or a metal oxide MOx deposited by Physical Vapor Deposition (PVD)) is then deposited in the nanotrench and capped by a PVD Cu-based top electrode (TE). Table I shows the samples (S1–S6) with materials stack variations, studied to understand the impact of each layer on the CBRAM operation. III. M ODEL D ESCRIPTION A. Device Operation Principle The CBRAM technology is based on the reversible formation and dissolution of a metallic CF inducing a change in the resistance state of the cell. This CF has been experimentally observed with conductive-AFM tomography [13]. The formation and dissolution process is known to be related to the oxidation and reduction of the metal composing the TE or BE [1], [2]. In this paper, the Cu-based TE is considered as the metal source for the CF creation. In order to initially create the CF in the first cycle, a high Forming voltage is required. This step allows the metallic ions to flow through the RL toward the opposite electrode and to create the CF. Similar to the SET operation and in opposition to the RESET, Forming consists in switching the cell from a high resistive state (HRS) to a low resistive state (LRS). After the Forming step, the cell operates at lower voltages. Once the data are stored, the main concern stands in its retention over time. Although the CBRAM is considered as a nonvolatile memory, the CF tends to dissolve itself over time, which ultimately leads to the loss of data. The CF dissolution rate is activated in temperature. Both the LRS and the HRS are potentially unstable states and

3483

Fig. 2. Particle transition mechanisms with the activation energy modified by electric field and energy level difference. (a)–(d) E A is the energy barrier height, q is the electrical charge of the hopping entity, V is the electrical potential, and dhopping is the hopping distance.

Fig. 3. Simulated physical mechanisms considered in the KMC environment.

have to be carefully studied to evaluate the reliability of the memory device [6]. B. KMC Environment The aim of our simulation framework is to create a consistent environment, in which Forming, SET, and Retention can be simulated by sharing the same physical parameters of the materials. The model is based on atomic migration and oxidation reduction reactions treated at the atomic and electronic level. Each and every atom is treated individually, according to their electrical and thermal environment. The CBRAM operation results in a succession of various physical mechanisms which ultimately leads to a change in resistance state. To simulate the CBRAM operation, the KMC model relies on a 2-D grid corresponding to every possible atomic and ionic positions inside the RL. The grid parameter (hopping distance between two adjacent points) is defined by the atomic Cu–Cu distance in the RL. Some insights into this latter can be obtained by the ab i ni ti o calculations [12], [14] and depends on the RL. C. Physical Mechanisms and KMC Interpretations 1) Redox Reaction and Ionic Migration: Both redox reactions and ionic migration can be correlated with a particle transitioning from an energy state to a more favorable one, overcoming an energy barrier [15] (Fig. 2). The oxidation corresponds to the transfer of an electron from a Cu atom at the edge of the RL to a neighboring metal atom. Similarly, the reduction is considered as the transfer of an electron to a Cu+ ionized atom from the BE or from the growing filament. Fig. 3 shows the different mechanisms previously stated and the environment, in which they take place. The

3484


particles hopping and redox reactions are described and modeled through the transition-state theory (TST), which introduces the mechanism rates [16], under the name of Eyring–Polanyi equation, in agreement with the Maxwell–Boltzmann statistic EA Γ = v exp − (1) kBT where Γ is the mechanism rate, v is the vibration constant, E A is the energy barrier height, k B is the Boltzmann constant, and T is the temperature. Considering the filament unformed, the joule heating is neglected and the temperature is fixed by the external conditions. The mechanism rate expressed in s −1 corresponds to the inverse of the required time for a mechanism defined by its activation energy E A to occur. E A depends on the involved mechanism and can be modulated (Fig. 2) by the applied electric field as well as by the difference of energy level between the two states, following: E A = E A −

Qd ∈ ΔE − 2 2

(2)

where E A is the modulated energy barrier height, Q is the particle charge, d is the physical distance between the two states, ∈ is the applied electric field, and ΔE is the energy difference between the two states. Moreover, it has been demonstrated [17] that the local electric field experienced inside an oxide is modified by a polarization of the atomic bonds. The local electric field can be expressed as ∈local = ∈applied (1 + Lχ)

(3)

where L is the Lorentz factor, χ is the electric susceptibility χ = εr − 1, and εr is the relative permittivity. By combining (1)–(3), it is possible to obtain the complete mechanism rate equation for the CBRAM system E A − 12 (Qd(1 + Lχ) ∈ −ΔE) Γ = v exp − . (4) kB T In order to simplify, we fixed v = 1013 s−1 [10] and L = 1/3 [17]. We assume that the duration of each mechanism is much shorter than the time it takes before it occurs. The system can be considered as memoryless similar to atomic exponential decay. This allows us to express the probability P of each action for a time duration t as P = 1 − exp(−Γ t).

(5)

This equation bonds the physical parameters to the occurring probability of each mechanism. 2) Electrical Conduction Model: As shown in (4), the mechanisms are driven by the electric field and the temperature. The temperature being fixed by the external conditions, we only have to calculate the electric field on every points of the grid. Moreover, the current flowing through the cell is another important matter, as it permits the monitoring of the CF formation. In order to save computation time without losing much accuracy, we chose to calculate at the same time the current flowing through the cell and the electrical potential at every points of the grid. The current is calculated

Fig. 4.

KMC flowchart.

by solving the resistance grid between the atoms. We compute the resistance between the two atoms using the TAT √ ∗ 2m E C (6) (d − a) R = R0 exp 2 where R0 is the quantum resistance (12.9 k), m ∗ is the effective mass of electron for a specific RL, E C is the energy barrier for electron tunneling, d is the distance between the two atoms, and a is the contact distance between the two atoms. The resistance depends on the distance between the two atoms until they reach a contact position (d = a) leading to a quantum resistance. Using the atoms position, we create a resistive grid between the atoms and solve the Kirchhoff law to obtain the current flowing through the cell and the voltage at each atom location. Finally, with the finite difference method, we acquire the voltage value across the RL. The TAT model is in agreement with the previous statements [6] regarding the progressive decrease in resistance while increasing the Cu concentration into the RL. D. KMC Operating The KMC resolution follows the flowchart in Fig. 4. It starts by initializing the environment: grid, electrodes, and time. Then, it calculates the current, voltage, and field across the RL, then it deduces the rates of the various possible mechanisms. The Retention and Forming/SET regimes follow the same path, with the only difference being the driving force. The main driving force for the switching process is the voltage applied, whereas none is applied during the Retention, and only the temperature runs the process. Based on the computed mechanisms rates, an optimized iteration duration t is determined ln(1 − rand) (7) ΣΓ where rand is a randomly generated value (ranging between 0 and 1) and corresponds to the probability of at least one event occurring, and ΣΓ is the sum of all the implied mechanisms rates. From this iteration times, it computes all the mechanisms probabilities following (5). After normalizing all the probabilities, the occurring events are chosen depending on their probability weight and the iteration time, then the particles locations and states are updated. Finally, the current, t=−


Fig. 5.

3485

Simulated filament growth and corresponding tForming extraction.

voltage, and field across the RL are solved, and the model determines if another loop is required in a function of the switching or Retention conditions. In the end, the model is able to produce a full simulated data Retention or writing process (Fig. 5). IV. E XPERIMENTAL R ESULTS A. Forming and SET Analyses In this section, we analyze how each layer of the CBRAM stack (TE, RL, and BE) impacts the Forming and SET operations combining the characterizations results and the KMC simulation. The electrical characterizations have been performed using an Agilent parameter analyzer, offering a good control of the limiting current (compliance current), responsible for the LRS resistance. The Forming/SET voltages were obtained using quasi-static programming conditions, whereas the Forming/SET time were obtained using pulsed programming and extracted as [6]. In regard to the KMC, a SET can be simulated by creating a residual filament in the RL and follows the same trend as a Forming with the reduced initial RL thickness and resistance. A specific focus is given to the relation between the Forming time and the voltage. Indeed, the tForming (VPulse ) characteristics show an exponential dependence, meaning that increasing the programming pulse drastically reduces the required Forming time. Moreover, the slope of these characteristics illustrates the time–voltage dilemma of resistive RAMs; a sharp slope indicating that short programming times at high voltage and good disturb immunity at low voltage may be combined. 1) Impact of the Top Electrode: Pure Cu as well as CuTex TE, both with the same TiN BE were investigated, the restrained Te concentration allows us to consider a unique energy barrier height of Cu redox reactions [18], and ionic hopping energy barrier height in the RL. The only variable we consider by changing the TE is the metal work function (TE ), with, respectively, 5.2 and 5 eV for Cu and CuTex . Fig. 6 shows the tForming (VPulse) experimental and simulated curves. We can see that an increase of TE by 0.2 eV leads to a tForming (VPulse ) shift of 0.25 V toward lower voltages. The TE work function has a double impact on the switching operation (Fig. 6). Indeed, the difference of work function between the TE and the BE induces a flat band voltage VFB

Fig. 6. (a) Measured and simulated Forming time as a function of the applied voltage for CBRAM integrating Cu or CuTex (S1 versus S2—Table I) as the TE (ion supply layer). (b) Schematics illustrating the impact of TE on the electric field in the RL and on the redox reaction.

which is reduced as TE increases VFB = BE − TE .

(8)

This flat band voltage adds up to the applied voltage and influences all the CBRAM physical mechanisms. Its impact is related to the RL thickness. However, the Fermi level of the TE corresponds to the final state of the electrons during oxidation. A higher TE leads to a lower Fermi level and a faster oxidation reaction in agreement with (4), independently of the RL thickness. The two influences of TE are antagonist, and the later one is dominant here as an increase of TE induces a diminution of tForming . 2) Impact of the Bottom Electrode: For the BE impact, we focused the study on two materials: TiN- and Si-doped W, both with the same CuTex TE. These materials have two different work functions, respectively, 4.8 and 4.4 eV. Fig. 7 shows that an increase of BE by 0.4 eV leads to a tForming (VPulse ) shift of 0.5 V toward lower voltages. Once again, the electrode work function has two effects on the switching operation (Fig. 7). First, a higher BE leads to a lower Fermi level (initial electron energy during reduction reaction) and a slower reduction reaction of the first ions reaching the BE (4). Then, a higher BE increases VFB (8), which accelerates the oxidation reaction. Seeing that the increase of BE reduces tForming, we can conclude that the limiting mechanisms in regard of the redox reaction are the oxidation at the TE. 3) Impact of the Resistive Layer: The RL has a much wider and complicated impact on the switching process as many mechanisms are involved. First, Fig. 8 shows an experimental

3486


Fig. 7. (a) Measured and simulated Forming time as a function of the applied voltage for CBRAM integrating WSi or TiN (S3 versus S2—Table I) as the BE (inert electrode). (b) Schematics illustrating the impact of BE on the electric field in the RL and on the redox reaction.

Fig. 8. (a) Measured and simulated Forming time as a function of the applied voltage for the 3.5 nm (S2) and 5 nm (S4) RL thickness. (b) Schematics illustrating the impact of the RL thickness on the electric field.

shift and a change of slope of tForming (VPulse ) toward lower voltages by reducing the RL thickness, in agreement with our simulations. An RL thickness reduction of 1.5 nm turns into a reduction of Forming voltage between 1.25 V (for long

Fig. 9. (a) Measured and simulated Forming time as a function of the pulse voltage for Al2 O3 -based (S4) and MO x -based (S5) CBRAM. (b) Schematics illustrating the impact of RL on the redox reaction and the hopping distance difference.

Forming times of 100 ms) and 2 V (for short Forming times of 100 ns) in the studied area. The electric field is tied to the voltage and distance by ∈ = d −1 V. For the sake of simplification and to clarify this point, the distance d can be assimilated to the RL thickness. This means that the electric field increases faster with voltage for a thinner RL. The change of slope of tForming(VPulse ) can thus be explained by an enhancement of the voltage impact on the electric field for a thin RL. Moreover, the reduction of the RL thickness translates into a shorter ionic migration path and a faster switching, in part responsible for the curve shift. We have then compared the Forming characteristics of Al2 O3 and MOx RL. A steeper slope and a faster behavior are measured for MOx , as shown in Fig. 9. The redox reactions take place at the interface between the RL and the electrodes. The RL plays a great role in these reactions, and regarding the TST affects the initial and final energy levels of the electrons during the redox reactions. Indeed, during the oxidation reaction, the electrons initial energy state corresponds to the energy level of Cu atom traps inside the RL. Moreover, regarding the reduction reaction, the electrons final energy state corresponds to the energy level of Cu ions trap in the RL. The trap depths strongly depend on the RL and can be extracted from the ab i ni ti o calculations [12]. In addition, the trap depths directly influence the reaction, as the difference of energy levels between the initial and final electrons state accelerates the reaction (4). Thus, deep Cu atom traps (initial state) induce a low energy difference between the


Fig. 10.

3487

Summary of the impact of each physical parameter of the CBRAM stack on the time–voltage Forming characteristics.

initial and final electrons states and lead to a slow oxidation reaction [Fig. 9(b)]. On the contrary, deep Cu ion traps increase the energy states difference and increase the reduction reaction speed. These trap depths can shift the tForming (VPulse ) curve but cannot explain the change of slope, as shown in Fig. 9. To address the change of slope, we have to look into parameters enhancing the electric field. By referring to (4), we see that the local electric field is enhanced by the electric permittivity as well as by the distance between two transition states of the TST. Both of these parameters are different for the two RL and participate to the change of slope: the higher the permittivity and hopping distance, the bigger the slope. The distance between the two states can be extracted by the ab i ni ti o calculation, as previously stated, and is affected by the RL density. It corresponds to the average value of the Cu–Cu distance in the RL for the ionic migration and in the interfacial region for the redox reactions. 4) SET and Forming—Conclusion: It has been demonstrated that by tuning the material stack and properties, we are able to control the Forming and thus the SET operation in two ways, as shown in Fig. 10. It is possible to shift the tForming (VPulse ) characteristic by tuning the energy levels of the various layers (RL , TE , and BE ) but also to change the characteristics slope by altering the RL features enhancing the electric field (thickness, permittivity, and hopping distance). Both the shift and slope changes of the tForming (VPulse ) characteristics offer a way to increase the SET/Forming speed (or to reduce the voltage), but in addition, the slope tuning brings on a way to improve the disturb immunity. This disturb immunity corresponds to the switching time at low voltages (typically Vread ) and can be tied to a better HRS Retention. The disturb immunity is improved for high dielectric constant

Fig. 11. Calculation of the SET time/voltage dependence for various residual filaments in the RL, corresponding to various initial ROFF values.

and thin RL. Finally, it is possible to extrapolate VSET from a different initial filament height corresponding to various initial HRS resistances, as shown in Fig. 11. B. Data Retention Modeling As previously stated, the goal of the KMC model is to create a unique environment for Forming/SET and Data Retention. This means that the observed behavior for the HRS and LRS Retention is simulated with the same physical properties and parameters as for the switching. The only difference resides in the main accelerating factor, being the voltage for Forming/ SET and the temperature for Data Retention. Fig. 12 shows the measured and simulated 100 °C Retention characteristics for both the HRS and the LRS. The impact of the compliance current used during the Forming/SET on the LRS Retention capabilities can be seen in accordance with [6]: the higher the compliance current, the lower the initial resistance, and the

3488


best Retention window possible for the targeted application and temperature. The programming routine of a CBRAM can then be adapted to achieve either low-power programming and moderate-temperature operation or higher programming power and high-temperature operation [19]. V. C ONCLUSION

Fig. 12. LRS and one HRS Data Retention experimental results and KMC simulations showing the impact of the initial RON on the Retention capabilities at 100 °C and the dissolution of the CF. The yellow curves share the same LRS resistance. 1: initial HRS. 2: final HRS. 3: final LRS. 4: initial LRS. All corresponding to the data: Rinit−2 .

We presented a KMC simulation able to recreate Forming, SET operation, and Data Retention of a CBRAM at atomic level. Good agreement with the experimental data obtained on decanometric CBRAM devices has been shown. From the joint study of modeling and electrical characterization, we described the relationship between the physical parameters of each layer and the device performance. Therefore, we presented a way to optimize the device in order to improve the time– voltage dilemma. Finally, we illustrated the KMC simulation ability to reproduce a Retention study from the simulated SET depending on the temperature accelerating the process. R EFERENCES

Fig. 13. LRS Data Retention experimental results and KMC simulations showing the impact of the temperature in the Retention capabilities and the dissolution of the CF.

better the Retention. This can be explained by the fact that the core of the filament, responsible for the ohmic conduction, remains intact longer for a thick filament, which enhances its Retention capabilities. Then, HRS is described by the presence of a residual filament, whose height depends on the HRS resistance, in agreement with previous statements [6]. HRS resistance decreases over time as the dissolution of the remaining filament tends to recreate a conducting path in the RL. The reformed conductive path can be seen (Fig. 12) on the final HRS showing only little differences with the final LRS. Moreover, as stated before, the temperature pushes the Retention operation. Fig. 13 shows the temperature impact on the Retention with the corresponding final states. We can see an increase of resistance value with the time spent at high temperature. The resistance increase corresponds to the loss of data over time and is due to the CF dissolution. This dissolution is much more visible for high temperature similar to the resistance increase. The higher the temperature, the more dissolved the CF, and the worst the Retention. It is possible to extract an activation energy of 1.2 eV from the experimental and simulated data. The KMC Retention simulation shows great results in agreement with the studied devices, in term of temperature and compliance current dependence on both the LRS and the HRS (not shown here) [6]. Its ability to predict the LRS and HRS behavior at various temperatures offers a way to optimize the SET conditions to obtain the

[1] R. Waser and M. Aono, “Nanoionics-based resistive switching memories,” Nature Mater., vol. 6, no. 11, pp. 833–840, Nov. 2007. [2] R. Waser, R. Dittmann, G. Staikov, and K. Szot, “Redox-based resistive switching memories—Nanoionic mechanisms, prospects, and challenges,” Adv. Mater., vol. 21, nos. 25–26, pp. 2632–2663, Jul. 2009. [3] R. Soni, M. Meier, A. Rüdiger, B. Holländer, C. Kügeler, and R. Waser, “Integration of ‘Gex Se1−x ’ in crossbar arrays for non-volatile memory applications,” Microelectron. Eng., vol. 86, nos. 4–6, pp. 1054–1056, Apr./Jun. 2009. [4] J. Guy et al., “Impact of Sb doping on power consumption and retention reliability of GeS2 based conductive bridge random access memory,” Thin Solid Films, vol. 563, pp. 15–19, Jul. 2014. [5] S. Maikap, S. Z. Rahaman, T. Y. Wu, F. Chen, M. J. Kao, and M. J. Tsai, “Low current (5 pA) resistive switching memory using high-k Ta2 O5 solid electrolyte,” in Proc. ESSDERC, Sep. 2009, pp. 217–220. [6] J. Guy et al., “Investigation of the physical mechanisms governing data-retention in down to 10 nm nano-trench Al2 O3 /CuTeGe conductive bridge RAM (CBRAM),” in IEDM Tech. Dig., Dec. 2013, pp. 30.2.1–30.2.4. [7] S. Sills et al., “A copper ReRAM cell for storage class memory applications,” in VLSI Tech. Dig., Jun. 2014, pp. 1–2. [8] J. Zahurak et al., “Process integration of a 27 nm, 16 Gb Cu ReRAM,” in IEDM Tech. Dig., Dec. 2014, pp. 6.2.1–6.2.4. [9] D. Ielmini, “Modeling the universal set/reset characteristics of bipolar RRAM by field- and temperature-driven filament growth,” IEEE Trans. Electron Devices, vol. 58, no. 12, pp. 4309–4317, Dec. 2011. [10] J. R. Jameson et al., “Effects of cooperative ionic motion on programming kinetics of conductive-bridge memory cells,” Appl. Phys. Lett., vol. 100, no. 2, p. 023505, 2012. [11] K. Sankaran et al., “Modeling of copper diffusion in amorphous aluminum oxide in CBRAM memory stack,” ECS Trans., vol. 45, no. 3, pp. 317–330, 2012. [12] J. Guy et al., “Experimental and theoretical understanding of forming, SET and RESET operations in conductive bridge ram (CBRAM) for memory stack optimization,” in IEDM Tech. Dig., Dec. 2014, pp. 6.5.1–6.5.4. [13] U. Celano, Y. Y. Chen, D. J. Wouters, G. Groeseneken, M. Jurczak, and W. Vandervorst, “Filament observation in metal-oxide resistive switching devices,” Appl. Phys. Lett., vol. 102, no. 12, p. 121602, 2013. [14] L. Goux et al., “Field-driven ultrafast sub-ns programming in WAl2 O3 TiCuTe-based 1T1R CBRAM system,” in VLSI Tech. Dig., Jun. 2012, pp. 69–70. [15] S. Larentis, F. Nardi, S. Balatti, D. C. Gilmer, and D. Ielmini, “Resistive switching by voltage-driven ion migration in bipolar RRAM—Part II: Modeling,” IEEE Trans. Electron Devices, vol. 59, no. 9, pp. 2468–2475, Sep. 2012. [16] K. J. Laidler and M. C. King, “Development of transition-state theory,” J. Phys. Chem., vol. 87, no. 15, pp. 2657–2664, 1983.


[17] J. W. McPherson, R. B. Khamankar, and A. Shanware, “Complementary model for intrinsic time-dependent dielectric breakdown in SiO2 dielectrics,” J. Appl. Phys., vol. 88, no. 9, pp. 5351–5359, Mar. 2000. [18] F. De Stefano et al., “Influence of metal electrode stoichiometry on the electron barrier height at Cux Te1−x /Al2 O3 interfaces for CBRAM applications,” Microelectron. Eng., vol. 120, pp. 9–12, May 2014. [19] M. Barci et al., “Impact of SET and RESET conditions on CBRAM high temperature data retention,” in Proc. Int. Rel. Phys. Symp., Jun. 2014, pp. 5E.3.1–5E.3.4.

Gilles Le Carval, photograph and biography not available at the time of publication.

Vincent Delaye, photograph and biography not available at the time of publication.

Alain Toffoli received the M.S. degree in engineering from the Conservatoire National des Arts et Metiers, Paris, France. He is currently the Head of the Advanced and Statistical Electrical Characterization Test Team with the Electrical Characterization and Test Laboratory, Commisariat à l’Énergie Atomique–Laboratoire d’Électronique et de Technologie de l’Information, Grenoble, France. His current research interests include test methodologies.

Jérémy Guy received the Engineering degree in material science from the Institut National des Sciences Appliquées de Lyon (INSA), Lyon, France, in 2012, and the M.S. degree in microelectronic and embedded system from INSA, and the University of Lyon, Lyon, in 2012. He is currently pursuing the Ph.D. degree with the Institut National Polytechnique de Grenoble, Grenoble, France. He joined Commisariat à l’Énergie Atomique– Laboratoire d’Électronique et de Technologie de l’Information, Grenoble, in 2012.

Gabriel Molas was born in Paris, France, in 1979. He received the B.S. and M.S. degrees in physics engineering with a specialization in microelectronics, and the Ph.D. degree in micro and nanoelectronics from the Polytechnics Institute of Grenoble, Grenoble, France, in 2001 and 2004, respectively, with a thesis in few electron memories. He joined Commisariat à l’Énergie Atomique– Laboratoire d’Électronique et de Technologie de l’Information, Grenoble, in 2004, as a Research Engineer.

Philippe Blaise received the master’s degree in applied mathematics from the École Nationale Supérieure d’Informatique et de Mathématiques Appliquées de Grenoble, Grenoble, France, and the Ph.D. degree in physics from Université Joseph Fourier, Saint-Martin-d’Hères, France, in 1998. He is currently a Permanent Researcher with Commisariat à l’Énergie Atomique–Laboratoire d’Électronique et de Technologie de l’Information, Grenoble.

3489

Gérard Ghibaudo received the Ph.D. and State Ph.D. degrees from the Institut de Microélectronique, Électromagnétisme et Photonique (IMEP)– Laboratoire d’Hyperfréquences et de Caractérisation (LAHC) Laboratory, MINATEC Center, Grenoble, France, in 1981 and 1984, respectively. He is currently the Director of Research with the Centre National de la Recherche Scientifique, IMEP–LAHC Laboratory, MINATEC Center. His current research interests include MOS device physics, fluctuations, and low-frequency noise and dielectric reliability.

Fabien Clermidy received the master’s degree, the Ph.D. degree in engineering science, and the Supervisor degree from the Grenoble Institute of Technology, Grenoble, France, in 1994, 1999, and 2011, respectively. He is currently the Head with the Digital Design Laboratory, Commisariat à l’Énergie Atomique– Laboratoire d’Électronique et de Technologie de l’Information, where he is involved in multicore architectures and design with a focus on emerging technologies.

Barbara De Salvo received the Ph.D. degree in microelectronics from the Polytechnic Institute of Grenoble, Grenoble, France. She has been a Scientist with Commisariat à l’Énergie Atomique–Laboratoire d’Électronique et de Technologie de l’Information, Grenoble, since 1999.

Mathieu Bernard, photograph and biography not available at the time of publication.

Anne Roule received the Engineer degree in microelectronics with a specialization in physics of materials from the Institut National des Sciences Appliquées de Lyon, Lyon, France. She joined Commisariat à l’Énergie Atomique– Laboratoire d’Électronique et de Technologie de l’Information, Grenoble, France, in 2003, as a Scientist, where she is currently responsible for advanced materials development.

Luca Perniola received the Laurea degree in nuclear engineering from the Politecnico di Milano, Milan, Italy, in 2002, and the Ph.D. degree from the University of Pisa, Pisa, Italy, and the Institut National Polytechnique de Grenoble, Grenoble, France, in 2005. He was with Commisariat à l’Énergie Atomique– Laboratoire d’Électronique et de Technologie de l’Information (LETI), Grenoble, in 2005. He has been appointed as the Head of the Advanced Memory Laboratory with LETI, since 2013.