- Award ID(s):
- 1544686
- Publication Date:
- NSF-PAR ID:
- 10119242
- Journal Name:
- Micromachines
- Volume:
- 10
- Issue:
- 7
- Page Range or eLocation-ID:
- 478
- ISSN:
- 2072-666X
- Sponsoring Org:
- National Science Foundation
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The traditional von Neumann architecture limits the increase in computing efficiency and results in massive power consumption in modern computers due to the separation of storage and processing units. The novel neuromorphic computation system, an in-memory computing architecture with low power consumption, is aimed to break the bottleneck and meet the needs of the next generation of artificial intelligence (AI) systems. Thus, it is urgent to find a memory technology to implement the neuromorphic computing nanosystem. Nowadays, the silicon-based flash memory dominates non-volatile memory market, however, it is facing challenging issues to achieve the requirements of future data storage device development due to the drawbacks, such as scaling issue, relatively slow operation speed, and high voltage for program/erase operations. The emerging resistive random-access memory (RRAM) has prompted extensive research as its simple two-terminal structure, including top electrode (TE) layer, bottom electrode (BE) layer, and an intermediate resistive switching (RS) layer. It can utilize a temporary and reversible dielectric breakdown to cause the RS phenomenon between the high resistance state (HRS) and the low resistance state (LRS). RRAM is expected to outperform conventional memory device with the advantages, notably its low-voltage operation, short programming time, great cyclic stability, and good scalability.more »
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The major focus of artificial intelligence (AI) research is made on biomimetic synaptic processes that are mimicked by functional memory devices in the computer industry [1]. It is urgent to find a memory technology for suiting with Brain-Inspired Computing to break the von Neumann bottleneck which limits the efficiency of conventional computer architectures [2]. Silicon-based flash memory, which currently dominates the market for data storage devices, is facing challenging issues to meet the needs of future data storage device development due to the limitations, such as high-power consumption, high operation voltage, and low retention capacity [1]. The emerging resistive random-access memory (RRAM) has elicited intense research as its simple sandwiched structure, including top electrode (TE) layer, bottom electrode (BE) layer, and an intermediate resistive switching (RS) layer, can store data using RS phenomenon between the high resistance state (HRS) and the low resistance state (LRS). This class of emerging devices is expected to outperform conventional memory devices [3]. Specifically, the advantages of RRAM include low-voltage operation, short programming time, great cyclic stability, and good scalability [4]. Among the materials for RS layer, indium gallium zinc oxide (IGZO) has attracted attention because of its abundance and high atomic diffusion property ofmore »
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Biomimetic synaptic processes, which are imitated by functional memory devices in the computer industry, are a key focus of artificial intelligence (AI) research. It is critical to developing a memory technology that is compatible with Brain-Inspired Computing in order to eliminate the von Neumann bottleneck that restricts the efficiency of traditional computer designs. Due to restrictions such as high operation voltage, poor retention capacity, and high power consumption, silicon-based flash memory, which presently dominates the data storage devices market, is having difficulty meeting the requirements of future data storage device development. The developing resistive random-access memory (RRAM) has sparked intense investigation because of its simple two-terminal structure: two electrodes and a switching layer. RRAM has a resistive switching phenomenon which is a cycling behavior between the high resistance state and the low resistance state. This developing device type is projected to outperform traditional memory devices. Indium gallium zinc oxide (IGZO) has attracted great attention for the RRAM switching layer because of its high transparency and high atomic diffusion property of oxygen atoms. More importantly, by controlling the oxygen ratio in the sputter gas, its electrical properties can be easily tuned. The IGZO has been applied to the thin-film transistor (TFT),more »
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Abstract Under varying growth and device processing conditions, ultrabroadband photoconduction (UBPC) reveals strongly evolving trends in the defect density of states (DoS) for amorphous oxide semiconductor thin‐film transistors (TFTs). Spanning the wide bandgap of amorphous InGaZnO
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