• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.5
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• pp.1061-1066
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• 2012

A digital memory has been widely used as a device for storing information due to its reliable, fast and relatively simple control circuit. However, the storage of the digital memory will be limited by the inablility to make smaller linewidths. One way to dramatically increase the storeage capability of the memory is to change the type of stored data from digital to analog. The analog memory fabricated in a standard single poly 0.6um CMOS process has been developed. Single cell and adjacent circuit block for programming have been designed and characterized. Applications include low-density non-volatile memory, control of redundancy in SRAM and DRAM memories, ID or security code registers, and image and sound memory.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.6
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• pp.425-432
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• 2004

In this paper, we introduce the analog memory fabricated in a 0.35${\mu}{\textrm}{m}$ single poly standard CMOS process. We measured the programming characteristics of the analog memory cell such as linearity, reliability etc. Finally, we found that the characteristics of the programming of the cell depend on the magnitude and the width of the programming pulse, and that the accuracy of the programming within 10mV is feasible under the optimal condition. In order to standardize the characteristics of the cell, we have investigated numbers of cells. Thus we have used a program named Labview and a data acquisition board to accumulate the data related to the programming characteristics automatically.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.354-361
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• 2023

Big data analysis systems apply NoSQL databases like MongoDB to store, process, and analyze diverse forms of large-scale data. MongoDB offers scalability and fast data processing speeds through distributed processing and data replication, depending on its configuration. This paper investigates the suitable MongoDB environment configurations for implementing big data analysis systems. For performance evaluation, we configured both single-node and multi-node environments. In the multi-node setup, we expanded the number of data nodes from two to three and measured the performance in each environment. According to the analysis, the processing speeds for complex data structures with three or more dimensions are approximately 5.75% faster in the single-node environment compared to an environment with two data nodes. However, a setting with three data nodes processes data about 25.15% faster than the single-node environment. On the other hand, for simple one-dimensional data structures, the multi-node environment processes data approximately 28.63% faster than the single-node environment. Further research is needed to practically validate these findings with diverse data structures and large volumes of data.