Emerging Electronics 

One of the efficient ways to store information is employing a material's resistivity as it can show a wide range of flexibility, in contrast to a conventional memory that is storing and releasing electrons. Applying for these benefits, we can construct high efficient electric devices empowering artificial intelligence.  

The ionic nature of oxides gives us a chance to modulate resistivity by applying a potential, resulting in drift/diffusion of mobile ions in the matrix. Under the applied potential to the sandwiched oxides between electrodes, mobile ions drift and/or diffuse, which will be determined by a boundary condition i.e., given concentration of electron, vacancy, or dopant and their conductivity. As a result of ionic drift/diffusion, the electronic structure of a matrix is changed so that the resistivity is modulated. Once we can use this phenomenon systematically, it will provide a huge potential for the era of AI.  

Investigating the ionic nature of oxides, we explore novel electronic devices empowering AI.

Related References

• H. G. Kim, V. R. Nallagatla, D.-H. Kwon, C. U. Jung, and M. Kim*, “In situ observations of topotactic phase transitions in a ferrite memristor,” J. Appl. Phys., 128, 7, (2020).  

• D.-H. Kwon, et al., “Unraveling the Origin and Mechanism of Nanofilament Formation in Polycrystalline SrTiO3 Resistive Switching Memories,” Adv. Mater., 31, 28, (2019). 

• J. Park†, D.-H. Kwon†, et al., “Role of oxygen vacancies in resistive switching in Pt/Nb-doped SrTiO3,” Appl. Phys. Lett., 105, 18, (2014). 

• D.-H. Kwon, et al., “Atomic structure of conducting nanofilaments in TiO2 resistive switching memory,” Nature Nanotechnology, 5, 2, (2010).