Oxide Interface-Based Polymorphic Electronic Devices for Neuromorphic Computing
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911585600012288 |
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| author | Pradhan, Soumen Miller, Kirill Hartmann, Fabian Spring, Merit Gabel, Judith Leikert, Berengar Kuhn, Silke Kamp, Martin Lopez-Richard, Victor Sing, Michael Claessen, Ralph Höfling, Sven |
| author_facet | Pradhan, Soumen Miller, Kirill Hartmann, Fabian Spring, Merit Gabel, Judith Leikert, Berengar Kuhn, Silke Kamp, Martin Lopez-Richard, Victor Sing, Michael Claessen, Ralph Höfling, Sven |
| contents | Aside from recent advances in artificial intelligence (AI) models, specialized AI hardware is crucial to address large volumes of unstructured and dynamic data. Hardware-based AI, built on conventional complementary metal-oxidesemiconductor (CMOS)-technology, faces several critical challenges including scaling limitation of devices [1, 2], separation of computation and memory units [3] and most importantly, overall system energy efficiency [4]. While numerous materials with emergent functionalities have been proposed to overcome these limitations, scalability, reproducibility, and compatibility remain critical obstacles [5, 6]. Here, we demonstrate oxide-interface based polymorphic electronic devices with programmable transistor, memristor, and memcapacitor functionalities by manipulating the quasi-two-dimensional electron gas in LaAlO3/SrTiO3 heterostructures [7, 8] using lateral gates. A circuit utilizing two polymorphic functionalities of transistor and memcapacitor exhibits nonlinearity and short-term memory, enabling implementation in physical reservoir computing. An integrated circuit incorporating transistor and memristor functionalities is utilized for the transition from short- to long-term synaptic plasticity and for logic operations, along with in-situ logic output storage. The same circuit with advanced reconfigurable synaptic logic operations presents high-level multi-input decision-making tasks, such as patient-monitoring in healthcare applications. Our findings pave the way for oxide-based monolithic integrated circuits in a scalable, silicon compatible, energy efficient single platform, advancing both the polymorphic and neuromorphic computings. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_03515 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Oxide Interface-Based Polymorphic Electronic Devices for Neuromorphic Computing Pradhan, Soumen Miller, Kirill Hartmann, Fabian Spring, Merit Gabel, Judith Leikert, Berengar Kuhn, Silke Kamp, Martin Lopez-Richard, Victor Sing, Michael Claessen, Ralph Höfling, Sven Disordered Systems and Neural Networks Materials Science Aside from recent advances in artificial intelligence (AI) models, specialized AI hardware is crucial to address large volumes of unstructured and dynamic data. Hardware-based AI, built on conventional complementary metal-oxidesemiconductor (CMOS)-technology, faces several critical challenges including scaling limitation of devices [1, 2], separation of computation and memory units [3] and most importantly, overall system energy efficiency [4]. While numerous materials with emergent functionalities have been proposed to overcome these limitations, scalability, reproducibility, and compatibility remain critical obstacles [5, 6]. Here, we demonstrate oxide-interface based polymorphic electronic devices with programmable transistor, memristor, and memcapacitor functionalities by manipulating the quasi-two-dimensional electron gas in LaAlO3/SrTiO3 heterostructures [7, 8] using lateral gates. A circuit utilizing two polymorphic functionalities of transistor and memcapacitor exhibits nonlinearity and short-term memory, enabling implementation in physical reservoir computing. An integrated circuit incorporating transistor and memristor functionalities is utilized for the transition from short- to long-term synaptic plasticity and for logic operations, along with in-situ logic output storage. The same circuit with advanced reconfigurable synaptic logic operations presents high-level multi-input decision-making tasks, such as patient-monitoring in healthcare applications. Our findings pave the way for oxide-based monolithic integrated circuits in a scalable, silicon compatible, energy efficient single platform, advancing both the polymorphic and neuromorphic computings. |
| title | Oxide Interface-Based Polymorphic Electronic Devices for Neuromorphic Computing |
| topic | Disordered Systems and Neural Networks Materials Science |
| url | https://arxiv.org/abs/2508.03515 |