Emerging 2D Ferroelectric Devices for In‐Sensor and In‐Memory Computing
The quantity of sensor nodes within current computing systems is rapidly increasing in tandem with the sensing data. The presence of a bottleneck in data transmission between the sensors, computing, and memory units obstructs the system's efficiency and speed. To minimize the latency of data tr...
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| Veröffentlicht in: | Advanced materials (Weinheim) Jg. 37; H. 2; S. e2400332 - n/a |
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01.01.2025
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| Abstract | The quantity of sensor nodes within current computing systems is rapidly increasing in tandem with the sensing data. The presence of a bottleneck in data transmission between the sensors, computing, and memory units obstructs the system's efficiency and speed. To minimize the latency of data transmission between units, novel in‐memory and in‐sensor computing architectures are proposed as alternatives to the conventional von Neumann architecture, aiming for data‐intensive sensing and computing applications. The integration of 2D materials and 2D ferroelectric materials has been expected to build these novel sensing and computing architectures due to the dangling‐bond‐free surface, ultra‐fast polarization flipping, and ultra‐low power consumption of the 2D ferroelectrics. Here, the recent progress of 2D ferroelectric devices for in‐sensing and in‐memory neuromorphic computing is reviewed. Experimental and theoretical progresses on 2D ferroelectric devices, including passive ferroelectrics‐integrated 2D devices and active ferroelectrics‐integrated 2D devices, are reviewed followed by the integration of perception, memory, and computing application. Notably, 2D ferroelectric devices have been used to simulate synaptic weights, neuronal model functions, and neural networks for image processing. As an emerging device configuration, 2D ferroelectric devices have the potential to expand into the sensor‐memory and computing integration application field, leading to new possibilities for modern electronics.
This work reviews the recent progress of 2D ferroelectric devices for in‐sensing and in‐memory neuromorphic computing. Experimental and theoretical progresses on 2D ferroelectric devices, including passive ferroelectrics‐integrated 2D devices and active ferroelectrics‐integrated 2D devices are reviewed followed by the integration of perception, memory, and computing application. Notably, the 2D ferroelectric devices have been used to simulate synaptic weights, neuronal model functions, and neural networks for image processing. |
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| AbstractList | The quantity of sensor nodes within current computing systems is rapidly increasing in tandem with the sensing data. The presence of a bottleneck in data transmission between the sensors, computing, and memory units obstructs the system's efficiency and speed. To minimize the latency of data transmission between units, novel in‐memory and in‐sensor computing architectures are proposed as alternatives to the conventional von Neumann architecture, aiming for data‐intensive sensing and computing applications. The integration of 2D materials and 2D ferroelectric materials has been expected to build these novel sensing and computing architectures due to the dangling‐bond‐free surface, ultra‐fast polarization flipping, and ultra‐low power consumption of the 2D ferroelectrics. Here, the recent progress of 2D ferroelectric devices for in‐sensing and in‐memory neuromorphic computing is reviewed. Experimental and theoretical progresses on 2D ferroelectric devices, including passive ferroelectrics‐integrated 2D devices and active ferroelectrics‐integrated 2D devices, are reviewed followed by the integration of perception, memory, and computing application. Notably, 2D ferroelectric devices have been used to simulate synaptic weights, neuronal model functions, and neural networks for image processing. As an emerging device configuration, 2D ferroelectric devices have the potential to expand into the sensor‐memory and computing integration application field, leading to new possibilities for modern electronics. The quantity of sensor nodes within current computing systems is rapidly increasing in tandem with the sensing data. The presence of a bottleneck in data transmission between the sensors, computing, and memory units obstructs the system's efficiency and speed. To minimize the latency of data transmission between units, novel in-memory and in-sensor computing architectures are proposed as alternatives to the conventional von Neumann architecture, aiming for data-intensive sensing and computing applications. The integration of 2D materials and 2D ferroelectric materials has been expected to build these novel sensing and computing architectures due to the dangling-bond-free surface, ultra-fast polarization flipping, and ultra-low power consumption of the 2D ferroelectrics. Here, the recent progress of 2D ferroelectric devices for in-sensing and in-memory neuromorphic computing is reviewed. Experimental and theoretical progresses on 2D ferroelectric devices, including passive ferroelectrics-integrated 2D devices and active ferroelectrics-integrated 2D devices, are reviewed followed by the integration of perception, memory, and computing application. Notably, 2D ferroelectric devices have been used to simulate synaptic weights, neuronal model functions, and neural networks for image processing. As an emerging device configuration, 2D ferroelectric devices have the potential to expand into the sensor-memory and computing integration application field, leading to new possibilities for modern electronics.The quantity of sensor nodes within current computing systems is rapidly increasing in tandem with the sensing data. The presence of a bottleneck in data transmission between the sensors, computing, and memory units obstructs the system's efficiency and speed. To minimize the latency of data transmission between units, novel in-memory and in-sensor computing architectures are proposed as alternatives to the conventional von Neumann architecture, aiming for data-intensive sensing and computing applications. The integration of 2D materials and 2D ferroelectric materials has been expected to build these novel sensing and computing architectures due to the dangling-bond-free surface, ultra-fast polarization flipping, and ultra-low power consumption of the 2D ferroelectrics. Here, the recent progress of 2D ferroelectric devices for in-sensing and in-memory neuromorphic computing is reviewed. Experimental and theoretical progresses on 2D ferroelectric devices, including passive ferroelectrics-integrated 2D devices and active ferroelectrics-integrated 2D devices, are reviewed followed by the integration of perception, memory, and computing application. Notably, 2D ferroelectric devices have been used to simulate synaptic weights, neuronal model functions, and neural networks for image processing. As an emerging device configuration, 2D ferroelectric devices have the potential to expand into the sensor-memory and computing integration application field, leading to new possibilities for modern electronics. The quantity of sensor nodes within current computing systems is rapidly increasing in tandem with the sensing data. The presence of a bottleneck in data transmission between the sensors, computing, and memory units obstructs the system's efficiency and speed. To minimize the latency of data transmission between units, novel in‐memory and in‐sensor computing architectures are proposed as alternatives to the conventional von Neumann architecture, aiming for data‐intensive sensing and computing applications. The integration of 2D materials and 2D ferroelectric materials has been expected to build these novel sensing and computing architectures due to the dangling‐bond‐free surface, ultra‐fast polarization flipping, and ultra‐low power consumption of the 2D ferroelectrics. Here, the recent progress of 2D ferroelectric devices for in‐sensing and in‐memory neuromorphic computing is reviewed. Experimental and theoretical progresses on 2D ferroelectric devices, including passive ferroelectrics‐integrated 2D devices and active ferroelectrics‐integrated 2D devices, are reviewed followed by the integration of perception, memory, and computing application. Notably, 2D ferroelectric devices have been used to simulate synaptic weights, neuronal model functions, and neural networks for image processing. As an emerging device configuration, 2D ferroelectric devices have the potential to expand into the sensor‐memory and computing integration application field, leading to new possibilities for modern electronics. This work reviews the recent progress of 2D ferroelectric devices for in‐sensing and in‐memory neuromorphic computing. Experimental and theoretical progresses on 2D ferroelectric devices, including passive ferroelectrics‐integrated 2D devices and active ferroelectrics‐integrated 2D devices are reviewed followed by the integration of perception, memory, and computing application. Notably, the 2D ferroelectric devices have been used to simulate synaptic weights, neuronal model functions, and neural networks for image processing. The quantity of sensor nodes within current computing systems is rapidly increasing in tandem with the sensing data. The presence of a bottleneck in data transmission between the sensors, computing, and memory units obstructs the system's efficiency and speed. To minimize the latency of data transmission between units, novel in‐memory and in‐sensor computing architectures are proposed as alternatives to the conventional von Neumann architecture, aiming for data‐intensive sensing and computing applications. The integration of 2D materials and 2D ferroelectric materials has been expected to build these novel sensing and computing architectures due to the dangling‐bond‐free surface, ultra‐fast polarization flipping, and ultra‐low power consumption of the 2D ferroelectrics. Here, the recent progress of 2D ferroelectric devices for in‐sensing and in‐memory neuromorphic computing is reviewed. Experimental and theoretical progresses on 2D ferroelectric devices, including passive ferroelectrics‐integrated 2D devices and active ferroelectrics‐integrated 2D devices, are reviewed followed by the integration of perception, memory, and computing application. Notably, 2D ferroelectric devices have been used to simulate synaptic weights, neuronal model functions, and neural networks for image processing. As an emerging device configuration, 2D ferroelectric devices have the potential to expand into the sensor‐memory and computing integration application field, leading to new possibilities for modern electronics. This work reviews the recent progress of 2D ferroelectric devices for in‐sensing and in‐memory neuromorphic computing. Experimental and theoretical progresses on 2D ferroelectric devices, including passive ferroelectrics‐integrated 2D devices and active ferroelectrics‐integrated 2D devices are reviewed followed by the integration of perception, memory, and computing application. Notably, the 2D ferroelectric devices have been used to simulate synaptic weights, neuronal model functions, and neural networks for image processing. |
| Author | Chen, Chunsheng Xu, Jianbin Pang, Yue Tong, Lei Zhou, Yaoqiang |
| AuthorAffiliation | 1 Department of Electronic Engineering and Materials Science and Technology Research Center The Chinese University of Hong Kong Hong Kong SAR China |
| AuthorAffiliation_xml | – name: 1 Department of Electronic Engineering and Materials Science and Technology Research Center The Chinese University of Hong Kong Hong Kong SAR China |
| Author_xml | – sequence: 1 givenname: Chunsheng surname: Chen fullname: Chen, Chunsheng organization: The Chinese University of Hong Kong – sequence: 2 givenname: Yaoqiang surname: Zhou fullname: Zhou, Yaoqiang organization: The Chinese University of Hong Kong – sequence: 3 givenname: Lei surname: Tong fullname: Tong, Lei organization: The Chinese University of Hong Kong – sequence: 4 givenname: Yue surname: Pang fullname: Pang, Yue organization: The Chinese University of Hong Kong – sequence: 5 givenname: Jianbin orcidid: 0000-0003-0509-9508 surname: Xu fullname: Xu, Jianbin email: jbxu@ee.cuhk.edu.hk organization: The Chinese University of Hong Kong |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38739927$$D View this record in MEDLINE/PubMed |
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| Keywords | ferroelectric device in‐memory computing in‐sensor computing neural network 2D materials |
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| SubjectTerms | 2D materials Data transmission ferroelectric device Ferroelectric materials Ferroelectricity Ferroelectrics Free surfaces Image processing in‐memory computing in‐sensor computing Memory devices Network latency neural network Neural networks Review Sensors Two dimensional materials |
| Title | Emerging 2D Ferroelectric Devices for In‐Sensor and In‐Memory Computing |
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