In resistive random access memory devices (RRAM), materials selection plays a critical role in the tuning of properties like switching voltages, retention, and endurance. Aside from choosing between transition metal-oxide matrices like HfOx, TiOx, TaOx, NiOx, and AlOx, doping in the oxide can also b...
In resistive random access memory devices (RRAM), materials selection plays a critical role in the tuning of properties like switching voltages, retention, and endurance. Aside from choosing between transition metal-oxide matrices like HfOx, TiOx, TaOx, NiOx, and AlOx, doping in the oxide can also be a powerful tool for device optimization. However, only a handful of dopants have been studied in detail [1-3], and it is currently not known which will offer the greatest benefit for device performance. The filamentary switching mechanism in RRAM devices, in which charged oxygen vacancies (VO’s) coalesce into conductive filaments during switching, has been widely documented [4-9]. Dopants have been shown to affect the switching characteristics of RRAM devices by decreasing the formation energy of VO’s and potentially stabilizing conductive filaments [1-3]. In this investigation, first principles modeling is used to characterize 42 possible dopants in HfOx. In addition to quantifying their effects on HfOx devices, trends are extracted which can serve as general guidelines for doping in any transition metal-oxide, and which are applicable to conductive bridge random access memories (CBRAM) as well. This work identifies a critical new metric for predicting dopant behavior in RRAM and CBRAM: the relative favorability of the dopant forming on substitutional or interstitial sites in the oxide lattice. Dopants that form on interstitial sites, such as Ag, Cu, and Ni, also tend to form cation filaments and thus CBRAM devices. Conversely, dopants that strongly prefer to substitute onto the Hf lattice site are unlikely to produce competing cation filaments, leaving the VO-based switching mechanism intact. The relative favorability is also independent of dopant chemical potential, allowing a direct one-to-one comparison of different dopant species in HfOx. In this work, the quantity of relative stability is formulated and quantified over all 42 cation dopants. The results are shown in Fig. 1. The impact of these dopants on VO formation energy is also analyzed, as shown in Fig. 2. The effect of dopants on device properties is strongly dependent upon dopant species, and Fig.’s 2 and 3 clearly show that these effects follow predictable trends. Finally, the trends underlying dopant behavior are explored, and a model is developed to predict dopant behavior in the oxide system. The behavior of the dopant was found to vary predictably as a function of six key quantities: (1) the valence difference between the dopant and Hf, (2) the dopant atomic radius, (3) the dopant oxide enthalpy of formation, (4) the change in dopant coordination from native oxide to HfOx, (5) the magnetization of the doped system, and (6) the perturbation of neighboring ion charge states by the dopant. The derived composite function predicts the relative dopant site favorability with 95% accuracy. Since these quantities can all be calculated for different oxides, it is expected that these trends can be used as guidelines for doping in any transition metal-oxide for both RRAM and CBRAM applications. [1] D. Duncan, B. Magyari-Kope, and Y. Nishi, Appl. Phys. Lett. 108, 043501, 2016.[2] L. Zhao, S. W. Ryu, A. Hazeghi, D. Duncan, B. Magyari-Kope, and Y. Nishi, Tech. Digest VLSI Tech. Symp., T106-T107, 2013.[3] D. Ning, P. Hua, and W. Wei, Chin. Phys. B 23(10), 107306, 2014.[4] D. Duncan, B. Magyari-Kope, and Y. Nishi, Elec. Dev. Lett. 37 (4), 400-403, 2016. [5] B. Magyari-Kope, S.G. Park, H.-D. Lee, and Y. Nishi, J. Mater. Sci. 47, pp. 7498-7514, 2012.[6] S.G. Park, B. Magyari-Kope, and Y. Nishi, Phys. Rev. B, 82, 115109, 2010.[7] H.-D. Lee, B. Magyari-Kope, and Y. Nishi, Phys. Rev. B 81, 193202, 2010.<..
In resistive random access memory devices (RRAM), materials selection plays a critical role in the tuning of properties like switching voltages, retention, and endurance. Aside from choosing between transition metal-oxide matrices like HfOx, TiOx, TaOx, NiOx, and AlOx, doping in the oxide can also be a powerful tool for device optimization. However, only a handful of dopants have been studied in detail [1-3], and it is currently not known which will offer the greatest benefit for device performance. The filamentary switching mechanism in RRAM devices, in which charged oxygen vacancies (VO’s) coalesce into conductive filaments during switching, has been widely documented [4-9]. Dopants have been shown to affect the switching characteristics of RRAM devices by decreasing the formation energy of VO’s and potentially stabilizing conductive filaments [1-3]. In this investigation, first principles modeling is used to characterize 42 possible dopants in HfOx. In addition to quantifying their effects on HfOx devices, trends are extracted which can serve as general guidelines for doping in any transition metal-oxide, and which are applicable to conductive bridge random access memories (CBRAM) as well. This work identifies a critical new metric for predicting dopant behavior in RRAM and CBRAM: the relative favorability of the dopant forming on substitutional or interstitial sites in the oxide lattice. Dopants that form on interstitial sites, such as Ag, Cu, and Ni, also tend to form cation filaments and thus CBRAM devices. Conversely, dopants that strongly prefer to substitute onto the Hf lattice site are unlikely to produce competing cation filaments, leaving the VO-based switching mechanism intact. The relative favorability is also independent of dopant chemical potential, allowing a direct one-to-one comparison of different dopant species in HfOx. In this work, the quantity of relative stability is formulated and quantified over all 42 cation dopants. The results are shown in Fig. 1. The impact of these dopants on VO formation energy is also analyzed, as shown in Fig. 2. The effect of dopants on device properties is strongly dependent upon dopant species, and Fig.’s 2 and 3 clearly show that these effects follow predictable trends. Finally, the trends underlying dopant behavior are explored, and a model is developed to predict dopant behavior in the oxide system. The behavior of the dopant was found to vary predictably as a function of six key quantities: (1) the valence difference between the dopant and Hf, (2) the dopant atomic radius, (3) the dopant oxide enthalpy of formation, (4) the change in dopant coordination from native oxide to HfOx, (5) the magnetization of the doped system, and (6) the perturbation of neighboring ion charge states by the dopant. The derived composite function predicts the relative dopant site favorability with 95% accuracy. Since these quantities can all be calculated for different oxides, it is expected that these trends can be used as guidelines for doping in any transition metal-oxide for both RRAM and CBRAM applications. [1] D. Duncan, B. Magyari-Kope, and Y. Nishi, Appl. Phys. Lett. 108, 043501, 2016.[2] L. Zhao, S. W. Ryu, A. Hazeghi, D. Duncan, B. Magyari-Kope, and Y. Nishi, Tech. Digest VLSI Tech. Symp., T106-T107, 2013.[3] D. Ning, P. Hua, and W. Wei, Chin. Phys. B 23(10), 107306, 2014.[4] D. Duncan, B. Magyari-Kope, and Y. Nishi, Elec. Dev. Lett. 37 (4), 400-403, 2016. [5] B. Magyari-Kope, S.G. Park, H.-D. Lee, and Y. Nishi, J. Mater. Sci. 47, pp. 7498-7514, 2012.[6] S.G. Park, B. Magyari-Kope, and Y. Nishi, Phys. Rev. B, 82, 115109, 2010.[7] H.-D. Lee, B. Magyari-Kope, and Y. Nishi, Phys. Rev. B 81, 193202, 2010.<..
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