Hybrid 2D–CMOS microchips for memristive applications
Exploiting the excellent electronic properties of two-dimensional (2D) materials to fabricate advanced electronic circuits is a major goal for the semiconductor industry 1 , 2 . However, most studies in this field have been limited to the fabrication and characterization of isolated large (more than...
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Published in | Nature (London) Vol. 618; no. 7963; pp. 57 - 62 |
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Main Authors | , , , , , , , , , , , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
London
Nature Publishing Group UK
01.06.2023
Nature Publishing Group |
Subjects | |
Online Access | Get full text |
ISSN | 0028-0836 1476-4687 1476-4687 |
DOI | 10.1038/s41586-023-05973-1 |
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Abstract | Exploiting the excellent electronic properties of two-dimensional (2D) materials to fabricate advanced electronic circuits is a major goal for the semiconductor industry
1
,
2
. However, most studies in this field have been limited to the fabrication and characterization of isolated large (more than 1 µm
2
) devices on unfunctional SiO
2
–Si substrates. Some studies have integrated monolayer graphene on silicon microchips as a large-area (more than 500 µm
2
) interconnection
3
and as a channel of large transistors (roughly 16.5 µm
2
) (refs.
4
,
5
), but in all cases the integration density was low, no computation was demonstrated and manipulating monolayer 2D materials was challenging because native pinholes and cracks during transfer increase variability and reduce yield. Here, we present the fabrication of high-integration-density 2D–CMOS hybrid microchips for memristive applications—CMOS stands for complementary metal–oxide–semiconductor. We transfer a sheet of multilayer hexagonal boron nitride onto the back-end-of-line interconnections of silicon microchips containing CMOS transistors of the 180 nm node, and finalize the circuits by patterning the top electrodes and interconnections. The CMOS transistors provide outstanding control over the currents across the hexagonal boron nitride memristors, which allows us to achieve endurances of roughly 5 million cycles in memristors as small as 0.053 µm
2
. We demonstrate in-memory computation by constructing logic gates, and measure spike-timing dependent plasticity signals that are suitable for the implementation of spiking neural networks. The high performance and the relatively-high technology readiness level achieved represent a notable advance towards the integration of 2D materials in microelectronic products and memristive applications.
High-integration-density 2D–CMOS hybrid microchips for memristive applications are made demonstrating in-memory computation and electrical response suitable for the implementation of spiking neural networks representing an advance towards integration of 2D materials in microelectronic products and memristive applications. |
---|---|
AbstractList | Exploiting the excellent electronic properties of two-dimensional (2D) materials to fabricate advanced electronic circuits is a major goal for the semiconductor industry
1
,
2
. However, most studies in this field have been limited to the fabrication and characterization of isolated large (more than 1 µm
2
) devices on unfunctional SiO
2
–Si substrates. Some studies have integrated monolayer graphene on silicon microchips as a large-area (more than 500 µm
2
) interconnection
3
and as a channel of large transistors (roughly 16.5 µm
2
) (refs.
4
,
5
), but in all cases the integration density was low, no computation was demonstrated and manipulating monolayer 2D materials was challenging because native pinholes and cracks during transfer increase variability and reduce yield. Here, we present the fabrication of high-integration-density 2D–CMOS hybrid microchips for memristive applications—CMOS stands for complementary metal–oxide–semiconductor. We transfer a sheet of multilayer hexagonal boron nitride onto the back-end-of-line interconnections of silicon microchips containing CMOS transistors of the 180 nm node, and finalize the circuits by patterning the top electrodes and interconnections. The CMOS transistors provide outstanding control over the currents across the hexagonal boron nitride memristors, which allows us to achieve endurances of roughly 5 million cycles in memristors as small as 0.053 µm
2
. We demonstrate in-memory computation by constructing logic gates, and measure spike-timing dependent plasticity signals that are suitable for the implementation of spiking neural networks. The high performance and the relatively-high technology readiness level achieved represent a notable advance towards the integration of 2D materials in microelectronic products and memristive applications.
High-integration-density 2D–CMOS hybrid microchips for memristive applications are made demonstrating in-memory computation and electrical response suitable for the implementation of spiking neural networks representing an advance towards integration of 2D materials in microelectronic products and memristive applications. Exploiting the excellent electronic properties of two-dimensional (2D) materials to fabricate advanced electronic circuits is a major goal for the semiconductor industry1,2. However, most studies in this field have been limited to the fabrication and characterization of isolated large (more than 1 µm2) devices on unfunctional SiO2-Si substrates. Some studies have integrated monolayer graphene on silicon microchips as a large-area (more than 500 µm2) interconnection3 and as a channel of large transistors (roughly 16.5 µm2) (refs. 4,5), but in all cases the integration density was low, no computation was demonstrated and manipulating monolayer 2D materials was challenging because native pinholes and cracks during transfer increase variability and reduce yield. Here, we present the fabrication of high-integration-density 2D-CMOS hybrid microchips for memristive applications-CMOS stands for complementary metal-oxide-semiconductor. We transfer a sheet of multilayer hexagonal boron nitride onto the back-end-of-line interconnections of silicon microchips containing CMOS transistors of the 180 nm node, and finalize the circuits by patterning the top electrodes and interconnections. The CMOS transistors provide outstanding control over the currents across the hexagonal boron nitride memristors, which allows us to achieve endurances of roughly 5 million cycles in memristors as small as 0.053 µm2. We demonstrate in-memory computation by constructing logic gates, and measure spike-timing dependent plasticity signals that are suitable for the implementation of spiking neural networks. The high performance and the relatively-high technology readiness level achieved represent a notable advance towards the integration of 2D materials in microelectronic products and memristive applications.Exploiting the excellent electronic properties of two-dimensional (2D) materials to fabricate advanced electronic circuits is a major goal for the semiconductor industry1,2. However, most studies in this field have been limited to the fabrication and characterization of isolated large (more than 1 µm2) devices on unfunctional SiO2-Si substrates. Some studies have integrated monolayer graphene on silicon microchips as a large-area (more than 500 µm2) interconnection3 and as a channel of large transistors (roughly 16.5 µm2) (refs. 4,5), but in all cases the integration density was low, no computation was demonstrated and manipulating monolayer 2D materials was challenging because native pinholes and cracks during transfer increase variability and reduce yield. Here, we present the fabrication of high-integration-density 2D-CMOS hybrid microchips for memristive applications-CMOS stands for complementary metal-oxide-semiconductor. We transfer a sheet of multilayer hexagonal boron nitride onto the back-end-of-line interconnections of silicon microchips containing CMOS transistors of the 180 nm node, and finalize the circuits by patterning the top electrodes and interconnections. The CMOS transistors provide outstanding control over the currents across the hexagonal boron nitride memristors, which allows us to achieve endurances of roughly 5 million cycles in memristors as small as 0.053 µm2. We demonstrate in-memory computation by constructing logic gates, and measure spike-timing dependent plasticity signals that are suitable for the implementation of spiking neural networks. The high performance and the relatively-high technology readiness level achieved represent a notable advance towards the integration of 2D materials in microelectronic products and memristive applications. Exploiting the excellent electronic properties of two-dimensional (2D) materials to fabricate advanced electronic circuits is a major goal for the semiconductor industry . However, most studies in this field have been limited to the fabrication and characterization of isolated large (more than 1 µm ) devices on unfunctional SiO -Si substrates. Some studies have integrated monolayer graphene on silicon microchips as a large-area (more than 500 µm ) interconnection and as a channel of large transistors (roughly 16.5 µm ) (refs. ), but in all cases the integration density was low, no computation was demonstrated and manipulating monolayer 2D materials was challenging because native pinholes and cracks during transfer increase variability and reduce yield. Here, we present the fabrication of high-integration-density 2D-CMOS hybrid microchips for memristive applications-CMOS stands for complementary metal-oxide-semiconductor. We transfer a sheet of multilayer hexagonal boron nitride onto the back-end-of-line interconnections of silicon microchips containing CMOS transistors of the 180 nm node, and finalize the circuits by patterning the top electrodes and interconnections. The CMOS transistors provide outstanding control over the currents across the hexagonal boron nitride memristors, which allows us to achieve endurances of roughly 5 million cycles in memristors as small as 0.053 µm . We demonstrate in-memory computation by constructing logic gates, and measure spike-timing dependent plasticity signals that are suitable for the implementation of spiking neural networks. The high performance and the relatively-high technology readiness level achieved represent a notable advance towards the integration of 2D materials in microelectronic products and memristive applications. Exploiting the excellent electronic properties of two-dimensional (2D) materials to fabricate advanced electronic circuits is a major goal for the semiconductor industry1,2. However, most studies in this field have been limited to the fabrication and characterization of isolated large (more than 1 µm2) devices on unfunctional SiO2-Si substrates. Some studies have integrated monolayer graphene on silicon microchips as a large-area (more than 500 µm2) interconnection3 and as a channel of large transistors (roughly 16.5 µm2) (refs. 4,5), but in all cases the integration density was low, no computation was demonstrated and manipulating monolayer 2D materials was challenging because native pinholes and cracks during transfer increase variability and reduce yield. Here, we present the fabrication of high-integration-density 2D-CMOS hybrid microchips for memristive applications-CMOS stands for complementary metal-oxide-semiconductor. We transfer a sheet of multilayer hexagonal boron nitride onto the back-end-of-line interconnections of silicon microchips containing CMOS transistors of the 180 nm node, and finalize the circuits by patterning the top electrodes and interconnections. The CMOS transistors provide outstanding control over the currents across the hexagonal boron nitride memristors, which allows us to achieve endurances of roughly 5 million cycles in memristors as small as 0.053 µm2. We demonstrate in-memory computation by constructing logic gates, and measure spike-timing dependent plasticity signals that are suitable for the implementation of spiking neural networks. The high performance and the relatively-high technology readiness level achieved represent a notable advance towards the integration of 2D materials in microelectronic products and memristive applications. Exploiting the excellent electronic properties of two-dimensional (2D) materials to fabricate advanced electronic circuits is a major goal for the semiconductor industry 1,2 . However, most studies in this field have been limited to the fabrication and characterization of isolated large (more than 1 µm 2 ) devices on unfunctional SiO 2 –Si substrates. Some studies have integrated monolayer graphene on silicon microchips as a large-area (more than 500 µm 2 ) interconnection 3 and as a channel of large transistors (roughly 16.5 µm 2 ) (refs. 4,5 ), but in all cases the integration density was low, no computation was demonstrated and manipulating monolayer 2D materials was challenging because native pinholes and cracks during transfer increase variability and reduce yield. Here, we present the fabrication of high-integration-density 2D–CMOS hybrid microchips for memristive applications—CMOS stands for complementary metal–oxide–semiconductor. We transfer a sheet of multilayer hexagonal boron nitride onto the back-end-of-line interconnections of silicon microchips containing CMOS transistors of the 180 nm node, and finalize the circuits by patterning the top electrodes and interconnections. The CMOS transistors provide outstanding control over the currents across the hexagonal boron nitride memristors, which allows us to achieve endurances of roughly 5 million cycles in memristors as small as 0.053 µm 2 . We demonstrate in-memory computation by constructing logic gates, and measure spike-timing dependent plasticity signals that are suitable for the implementation of spiking neural networks. The high performance and the relatively-high technology readiness level achieved represent a notable advance towards the integration of 2D materials in microelectronic products and memristive applications. |
Author | Zhang, Xixiang Zhu, Kaichen Shen, Yaqing Alharbi, Osamah Roldan, Juan B. Zheng, Wenwen Li, Ren Farronato, Matteo Lanza, Mario Aguirre, Fernando Fang, Bin Wang, Tao Fariborzi, Hossein Yuan, Yue Benstetter, Guenther Wu, Huaqiang Muñoz-Rojo, Miguel Grasser, Tibor Ielmini, Daniele Milozzi, Alessandro Alshareef, Husam N. Villena, Marco A. Pazos, Sebastian Li, Xinyi |
Author_xml | – sequence: 1 givenname: Kaichen surname: Zhu fullname: Zhu, Kaichen organization: Materials Science and Engineering Program, Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST) – sequence: 2 givenname: Sebastian orcidid: 0000-0002-7354-4530 surname: Pazos fullname: Pazos, Sebastian organization: Materials Science and Engineering Program, Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST) – sequence: 3 givenname: Fernando orcidid: 0000-0001-7793-1194 surname: Aguirre fullname: Aguirre, Fernando organization: Materials Science and Engineering Program, Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST) – sequence: 4 givenname: Yaqing surname: Shen fullname: Shen, Yaqing organization: Materials Science and Engineering Program, Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST) – sequence: 5 givenname: Yue surname: Yuan fullname: Yuan, Yue organization: Materials Science and Engineering Program, Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST) – sequence: 6 givenname: Wenwen surname: Zheng fullname: Zheng, Wenwen organization: Materials Science and Engineering Program, Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST) – sequence: 7 givenname: Osamah surname: Alharbi fullname: Alharbi, Osamah organization: Materials Science and Engineering Program, Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST) – sequence: 8 givenname: Marco A. orcidid: 0000-0001-5547-3380 surname: Villena fullname: Villena, Marco A. organization: Materials Science and Engineering Program, Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST) – sequence: 9 givenname: Bin surname: Fang fullname: Fang, Bin organization: Materials Science and Engineering Program, Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST) – sequence: 10 givenname: Xinyi surname: Li fullname: Li, Xinyi organization: Institute of Microelectronics, Tsinghua University – sequence: 11 givenname: Alessandro orcidid: 0000-0001-5207-1984 surname: Milozzi fullname: Milozzi, Alessandro organization: Department of Electronics, Information and Bioengineering, Politecnico of Milan – sequence: 12 givenname: Matteo orcidid: 0000-0003-1122-6497 surname: Farronato fullname: Farronato, Matteo organization: Department of Electronics, Information and Bioengineering, Politecnico of Milan – sequence: 13 givenname: Miguel orcidid: 0000-0001-9237-4584 surname: Muñoz-Rojo fullname: Muñoz-Rojo, Miguel organization: Department of Thermal and Fluid Engineering, Faculty of Engineering Technology, University of Twente, Institute of Micro and Nanotechnology, IMN-CNM, CSIC (CEI UAM+CSIC) – sequence: 14 givenname: Tao surname: Wang fullname: Wang, Tao organization: Institute of Functional Nano and Soft Materials, Collaborative Innovation Center of Suzhou Nanoscience and Technology, Soochow University – sequence: 15 givenname: Ren orcidid: 0000-0002-7504-2147 surname: Li fullname: Li, Ren organization: Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology – sequence: 16 givenname: Hossein orcidid: 0000-0002-7828-0239 surname: Fariborzi fullname: Fariborzi, Hossein organization: Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology – sequence: 17 givenname: Juan B. orcidid: 0000-0003-1662-6457 surname: Roldan fullname: Roldan, Juan B. organization: Department of Electronics and Computer Technology, Faculty of Sciences, University of Granada – sequence: 18 givenname: Guenther orcidid: 0000-0001-7625-1293 surname: Benstetter fullname: Benstetter, Guenther organization: Department of Electrical Engineering and Media Technology, Deggendorf Institute of Technology – sequence: 19 givenname: Xixiang orcidid: 0000-0002-3478-6414 surname: Zhang fullname: Zhang, Xixiang organization: Materials Science and Engineering Program, Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST) – sequence: 20 givenname: Husam N. orcidid: 0000-0001-5029-2142 surname: Alshareef fullname: Alshareef, Husam N. organization: Materials Science and Engineering Program, Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST) – sequence: 21 givenname: Tibor orcidid: 0000-0001-6536-2238 surname: Grasser fullname: Grasser, Tibor organization: Institute for Microelectronics, TU Wien – sequence: 22 givenname: Huaqiang orcidid: 0000-0001-8359-7997 surname: Wu fullname: Wu, Huaqiang organization: Institute of Microelectronics, Tsinghua University – sequence: 23 givenname: Daniele orcidid: 0000-0002-1853-1614 surname: Ielmini fullname: Ielmini, Daniele organization: Department of Electronics, Information and Bioengineering, Politecnico of Milan – sequence: 24 givenname: Mario orcidid: 0000-0003-4756-8632 surname: Lanza fullname: Lanza, Mario email: mario.lanza@kaust.edu.sa organization: Materials Science and Engineering Program, Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST) |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36972685$$D View this record in MEDLINE/PubMed |
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ContentType | Journal Article |
Copyright | The Author(s) 2023 2023. The Author(s). Copyright Nature Publishing Group Jun 1, 2023 |
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SubjectTerms | 639/166/987 639/301/357/1018 Alternation learning Boron Boron nitride Circuits CMOS Computation Density Electrodes Electronic circuits Electrons Fabrication Firing pattern Graphene Humanities and Social Sciences Integrated circuits Integration Interconnections Logic circuits Memristors Monolayers multidisciplinary Multilayers Neural networks Pinholes Science Science (multidisciplinary) Semiconductors Silicon Silicon dioxide Silicon substrates Silicon wafers Technology assessment Transistors Two dimensional materials |
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Title | Hybrid 2D–CMOS microchips for memristive applications |
URI | https://link.springer.com/article/10.1038/s41586-023-05973-1 https://www.ncbi.nlm.nih.gov/pubmed/36972685 https://www.proquest.com/docview/2822497592 https://www.proquest.com/docview/2819275814 https://pubmed.ncbi.nlm.nih.gov/PMC10232361 |
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