DC Resistance Degradation of SrTiO$_3$: The Role of Virtual-Cathode Needles and Oxygen Bubbles
Ana Alvarez, I-Wei Chen

TL;DR
This paper investigates the mechanisms behind resistance degradation in SrTiO$_3$, revealing the roles of virtual-cathode needles and oxygen bubbles, and introduces new insights into failure processes in ceramic oxide devices.
Contribution
It provides an analytical solution for oxygen-vacancy migration, identifies cathode-initiated needles as key degradation factors, and links oxygen bubbling to final failure in SrTiO$_3$ devices.
Findings
Needles are invisible but detectable via in-situ photography.
Oxygen bubbling correlates with accelerated degradation.
Electroluminescence indicates resistance transition.
Abstract
This study of highly accelerated lifetime tests of SrTiO, a model semiconducting oxide, is motivated by the interest in reliable multilayer ceramic capacitors and resistance-switching thin-film devices. Our analytical solution to oxygen-vacancy migration under a DC voltage -- the cause of resistance degradation in SrTiO -- agrees with previous numerical solutions. However, all solutions fail to explain why degradation kinetics feature a very strong voltage dependence, which we attribute to the nucleation and growth of cathode-initiated fast-conducting needles. While they have no color contrast in SrTiO single crystals and are nominally invisible, needles presence in DC-degraded samples -- in silicone oil and in air -- was unambiguously revealed by in-situ hot-stage photography. Observations in silicone oil and thermodynamic considerations of voltage boundary conditions…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsFerroelectric and Piezoelectric Materials · Electronic and Structural Properties of Oxides · Advanced Memory and Neural Computing
