Nitride Ferroelectric Domain Wall Memory for Next-Generation Computing
Research output: Contribution to journal › Research article › Contributed › peer-review
Contributors
Abstract
The emerging nitride ferroelectrics, such as Al1-xScxN promise to significantly advance our current information technology. In particular, two-terminal memristive devices are ideal candidates for artificial intelligence accelerators and in-memory computing due to their simplicity in design, non-volatility and non-destructive readout. The recent discovery of conductive domain walls in Al1-xScxN is a promising enabler for such technology, offering several benefits compared to barrier height modulation- or tunneling-based devices. First, domain walls can be highly conductive and feature high read currents (required for aggressive lateral scaling and fast access times), also in non-epitaxial films without being restricted to the technologically challenging ultrathin thickness regime ((Formula presented.) 10 nm). Second, nitride ferroelectrics are fully compatible with silicon and GaN technology on which the ferroelectric domain wall memory (FeDMEM) can be integrated with logic circuitry. Third, excellent scalability and temperature resistance of ferroelectric Al1-xScxN were demonstrated, enabling scaled, low-latency edge computing under extreme environmental conditions. In this study, a FeDMEM device consisting of a Pt/Al0.72Sc0.28N/Pt capacitor grown on Si substrates is electrically characterized in-depth, revealing unique peculiarities in the memristive response. A read current density of 350 A/m2 and an ON/OFF ratio of 20 is achieved, allowing for consistent storing of up to eight levels of information.
Details
| Original language | English |
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| Article number | e00616 |
| Journal | Advanced electronic materials |
| Publication status | E-pub ahead of print - 23 Dec 2025 |
| Peer-reviewed | Yes |
External IDs
| ORCID | /0000-0003-3814-0378/work/202352140 |
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Keywords
ASJC Scopus subject areas
Keywords
- domain wall conduction, Keywords: aluminum scandium nitride, memristive device, neuromorphic computing, nitride ferroelectrics