BROWSE
Detail
Journal Article
Multi‐Level Long‐Term Memory Resembling Human Memory Based on Photosensitive Field‐Effect Transistors with Stable Interfacial Deep Traps
Cited 10 time in 
Share 
Share
- Authors
- Taeyoon Kim, Jung Wook Lim, Sun Jin Yun, Seong Hyun Lee, Kwang Hoon Jung
- Issue Date
- 2020-04
- Citation
- Advanced Electronic Materials, v.66, no.4, pp.1-7
- ISSN
- 2199-160X
- Publisher
- Wiley
- Language
- English
- Type
- Journal Article
- Abstract
- The development of electronic devices that emulate the learning mechanism of the human brain is crucial for replicating the capabilities of the human brain. Photoelectric devices with learning capabilities are good candidates for input-energy-effective and multi-level memory devices. Herein, a novel TiO2-metaloxide-semiconductor field-effect transistor (MOSFET)-based memory device is reported, that effectively converts learned data to long-term memory (LTM) by longer stimulus intervals and realizes multi-state memory. This device is programmed by UV light and uses deep trap sites at the interface between the TiO2 and Al2O3 layers to retain data. Moreover, the current levels of LTM are determined by varying pulse width, light power density, and pulse number. The pulse interval is confirmed to be beneficial for converting input data to LTM without additional energy consumption. In particular, the unique characteristics of the device, which delivers a higher current with an increasing number of pulse intervals, describe the characteristics of the actual human brain, which is a feat that no other device has demonstrated. Furthermore, excellent multi-level storage capabilities and remarkable retention characteristics (≈30 000 s) of the TiO2?밠OSFET device are demonstrated. Thus, an advanced memory device that will contribute to next-generation artificial intelligence systems is realized.
- KSP Keywords
- Deep traps, Energy-effective, Field-effect transistors(FETs), Long-term memory, Memory-based, Multi-state, Next-generation, Number of pulse, Power Density, Pulse number, State memory