Nanocomposites and nanomaterials
 
Properties of materials for ResistiveRAM based on HfO2 (First principles calculations)
Balabai R.M., Zalevskyi D.V.
Kryvyi Rih State Pedagogical University, 54 Gagarina Ave., Kryvyi Rih, 50086, Ukraine.
E-mail: balabai@i.ua , denys.zalevski@gmail.com

The computing systems that run today’s artiﬁcial intelligence algorithms are based on the von Neumann architecture where large amounts of data need to be shuttled back and forth at high speeds during the execution of these computational tasks [1]. This creates a performance bottleneck and also leads to signiﬁcant area/power inefficiency. Thus, it is becoming increasingly clear that to build efficient cognitive computers, it is necessary to transition to novel architectures where memory and processing are better collocated. Resistive  switching  random  access  memory  (RRAM)  is  being  considered  as  a next-generation  non-volatile  memory  with  promising  potential  advantages,  such  as scalability, simple structure, fast operational speed, excellent endurance, long retention and low power consumption. Various material systems including inorganic metal oxides are popular candidates for RRAM fabrications. It was reported that devices based on Au-doped HfO2 show improved uniformity, increased ON/OFF ratio, and reduced set voltage [2]. Segregation energy trends and their charge state dependence has also been investigated for Group II to Group VI substitutional metal dopants in HfO2 using density functional theory [3]. In this paper the electronic structures of RRAM based on Hf02, charge transfer of atoms, and the interaction between dopants and own atoms are studied by methods of the electron density functional and first-principles pseudopotential based on own program code [4]. 

1.	Javier del Valle, Juan Gabriel Ramírez, Marcelo J. Rozenberg,and Ivan K. SchullerJ. Challenges in materials and devices for resistive-switching-based
neuromorphic computing// J. Appl. Phys.-2018.-124.-P. 211101-1-23.
2.	Tingting Tan, Ai Cao, Gangqiang Zha. A ﬁrst-principles study of the effects of Au dopants in HfO2–based RRAM // Superlattices and Microstructures.-2018.-121.-P. 38-44.
3.	Blanka Magyari-K¨ope, Yali Song, Dan Duncan, Liang Zhao, and Yoshio Nishi. Research Update: Ab initio study on resistive memory device optimization trends: Dopant segregation effects and data retention in HfO2−x // APL Materials.-2018.-6.-P. 058102-1-10.
4.	Ab initio calculation [E-resource] – Mode access to the resource: http://sites.google.com/a/kdpu.edu.ua/calculationphysics.