Ternary Heterojunction Synaptic Transistors Based on Perovskite Quantum Dots.
Saved in:
| Title: | Ternary Heterojunction Synaptic Transistors Based on Perovskite Quantum Dots. |
|---|---|
| Authors: | Lan, Shuqiong1 (AUTHOR) shuqionglan@jmu.edu.cn, Si, Jinkui1 (AUTHOR), Xu, Wangying1 (AUTHOR), Yang, Lan1 (AUTHOR), Lin, Jierui1 (AUTHOR), Wu, Chen1 (AUTHOR) |
| Source: | Nanomaterials (2079-4991). May2025, Vol. 15 Issue 9, p688. 10p. |
| Subjects: | Adaptive computing systems, Optoelectronic devices, Transistors, Heterojunctions, Energy consumption |
| Abstract: | The traditional von Neumann architecture encounters significant limitations in computational efficiency and energy consumption, driving the development of neuromorphic devices. The optoelectronic synaptic device serves as a fundamental hardware foundation for the realization of neuromorphic computing and plays a pivotal role in the development of neuromorphic chips. This study develops a ternary heterojunction synaptic transistor based on perovskite quantum dots to tackle the critical challenge of synaptic weight modulation in organic synaptic devices. Compared to binary heterojunction synaptic transistor, the ternary heterojunction synaptic transistor achieves an enhanced hysteresis window due to the synergistic charge-trapping effects of acceptor material and perovskite quantum dots. The memory window decreases with increasing source-drain voltage (VDS) but expands with prolonged program/erase time, demonstrating effective carrier trapping modulation. Furthermore, the device successfully emulates typical photonic synaptic behaviors, including excitatory postsynaptic currents (EPSCs), paired-pulse facilitation (PPF), and the transition from short-term plasticity (STP) to long-term plasticity (LTP). This work provides a simplified strategy for high-performance optoelectronic synaptic transistors, showcasing significant potential for neuromorphic computing and adaptive intelligent systems. [ABSTRACT FROM AUTHOR] |
| Copyright of Nanomaterials (2079-4991) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | The traditional von Neumann architecture encounters significant limitations in computational efficiency and energy consumption, driving the development of neuromorphic devices. The optoelectronic synaptic device serves as a fundamental hardware foundation for the realization of neuromorphic computing and plays a pivotal role in the development of neuromorphic chips. This study develops a ternary heterojunction synaptic transistor based on perovskite quantum dots to tackle the critical challenge of synaptic weight modulation in organic synaptic devices. Compared to binary heterojunction synaptic transistor, the ternary heterojunction synaptic transistor achieves an enhanced hysteresis window due to the synergistic charge-trapping effects of acceptor material and perovskite quantum dots. The memory window decreases with increasing source-drain voltage (VDS) but expands with prolonged program/erase time, demonstrating effective carrier trapping modulation. Furthermore, the device successfully emulates typical photonic synaptic behaviors, including excitatory postsynaptic currents (EPSCs), paired-pulse facilitation (PPF), and the transition from short-term plasticity (STP) to long-term plasticity (LTP). This work provides a simplified strategy for high-performance optoelectronic synaptic transistors, showcasing significant potential for neuromorphic computing and adaptive intelligent systems. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 20794991 |
| DOI: | 10.3390/nano15090688 |
