image: Block diagram of data storage augmentation technology of a flash memory device. During the deposition of the data storage layer, the argon plasma strongly collides to form many defects in the data storage layer. Many electrons can be stored in the generated defects, thus increasing data storage.
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Credit: POSTECH

Bumper cars are enjoyed by people of all ages, as drivers deliberately collide with other nearby vehicles. Recently, a new technology has emerged that dramatically improves flash memory performance through a powerful ion bombardment process. This memory platform can reliably express multiple data in a single device, making it applicable to future neuromorphic computing and increasing memory capacity.

Yoonyoung Chung, Professor at POSTECH (Department of Electrical Engineering and Department of Semiconductor Engineering) and Ph.D. Candidate Seongmin Park (Department of Electrical Engineering), in joint research with Samsung Electronics, developed flash memory with increased data storage by intentionally generating faults.

As the technology of artificial intelligence advances, there is a need to develop a new neural network-optimized semiconductor device with multi-level data. New materials and devices have been developed as neuromorphic devices, but have limitations in terms of durability, scalability, and storage capacity compared to flash memory, which has been widely used as a storage device for various applications .

To overcome these issues, the research team implemented a powerful plasma bombardment process during the deposition of the data storage layer to generate artificial defect sites in a flash memory device. The researchers confirmed that more electrons can be stored in the generated defects, significantly increasing the amount of data storage compared to conventional flash memory.

A multi-level data memory can be demonstrated when electrons gradually fill up in the data storage layer in which many defects are generated. The multilevel flash memory developed in this study can reliably distinguish eight levels of data.

The results of the study are significant in that they can minimize the risk of developing a new semiconductor material or structure and, at the same time, significantly advance flash memory with excellent performance and scalability. for AI applications. When applied to neuromorphic systems, the accuracy and reliability of inference are expected to be significantly improved compared to conventional devices.

Recently published in Materials Today Nanoa renowned international academic journal in the field of nanotechnology, this study was supported by Samsung Electronics and the Nextgeneration type of intelligence Semiconductor development program.


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