scholarly journals Soft pre-charge H/V switch for charge pump with NAND flash memory using external power

2013 ◽  
Vol 10 (17) ◽  
pp. 20130497-20130497
Author(s):  
Youngil Kim ◽  
Sangsun Lee
2010 ◽  
Vol 1250 ◽  
Author(s):  
Kousuke Miyaji ◽  
Teruyoshi Hatanaka ◽  
Shuhei Tanakamaru ◽  
Ryoji Yajima ◽  
Shinji Noda ◽  
...  

AbstractThis paper overview recent research results about ferroelectric FETs such as a Ferroelectric (Fe-) NAND flash memory for enterprise SSDs and a Ferroelectric 6T-SRAM for 0.5V operation low-power CPU and SoC.In the last five years, as the data through internet increases, the power consumption at the data center doubled. To solve the power crisis SSD is expected to replace HDD. For such an enterprise SSD, the Fe-NAND flash memory is most suitable due to a low power consumption and a high reliability. The Fe-NAND is composed of Metal Ferroelectric Insulator Semiconductor transistors. The program/erase voltage decreases from 20V to 6V. In the Fe-NAND, the electric polarization in the ferroelectric layer flips with a lower electric field and the Vth of a memory cell shifts. Due to a low program/erase voltage, a low power operation is achieved. In the Fe-NAND, a high write/erase endurance, 100Million cycle, four orders of magnitudes higher than the conventional NAND, is realized because there is no stress-induced leakage current.The Fe-NAND flash memory with a non-volatile (NV) page buffer is also proposed. The data fragmentation of SSD in a random write is removed by introducing a batch write algorithm. As a result, the SSD performance can double. The NV-page buffer realizes a power outage immune highly reliable operation. In addition, a zero Vth memory cell scheme is proposed to best optimize the reliability of the Fe-NAND. The Vth shift caused by the read disturb, program disturb and data retention decreases by 32%, 24% and 10%, respectively. A 1.2V operation adaptive charge pump circuit for the low voltage and low power Fe-NAND is introduced. By using Fe-FETs as diodes in the charge pump and optimizing the Vth of Fe-FETs at each pump stage, the power efficiency and the output voltage increase by 143% and 25% without the circuit area and process step penalty.Finally, a ferroelectric 6T-SRAM is proposed for the 0.5V operation low power CPU and SoC. During the read/hold, the Vth of Fe-FETs automatically changes to increase the static noise margin by 60%. During the stand-by, the Vth increases to decrease the leakage current by 42%. As a result, the supply voltage by 0.11V, which decreases the active power by 32%.


Author(s):  
Liyin Fu ◽  
Yu Wang ◽  
Qi Wang ◽  
Shiyang Yang ◽  
Yan Yang ◽  
...  

2016 ◽  
Vol 37 (7) ◽  
pp. 075001
Author(s):  
Liyin Fu ◽  
Yu Wang ◽  
Qi Wang ◽  
Zongliang Huo

2011 ◽  
Vol 8 (16) ◽  
pp. 1343-1347 ◽  
Author(s):  
SungWook Choi ◽  
DuckJu Kim ◽  
JunSeob Chung ◽  
BongSeok Han ◽  
JeaGun Park

2012 ◽  
Vol E95.C (5) ◽  
pp. 837-841 ◽  
Author(s):  
Se Hwan PARK ◽  
Yoon KIM ◽  
Wandong KIM ◽  
Joo Yun SEO ◽  
Hyungjin KIM ◽  
...  

Micromachines ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 879
Author(s):  
Ruiquan He ◽  
Haihua Hu ◽  
Chunru Xiong ◽  
Guojun Han

The multilevel per cell technology and continued scaling down process technology significantly improves the storage density of NAND flash memory but also brings about a challenge in that data reliability degrades due to the serious noise. To ensure the data reliability, many noise mitigation technologies have been proposed. However, they only mitigate one of the noises of the NAND flash memory channel. In this paper, we consider all the main noises and present a novel neural network-assisted error correction (ANNAEC) scheme to increase the reliability of multi-level cell (MLC) NAND flash memory. To avoid using retention time as an input parameter of the neural network, we propose a relative log-likelihood ratio (LLR) to estimate the actual LLR. Then, we transform the bit detection into a clustering problem and propose to employ a neural network to learn the error characteristics of the NAND flash memory channel. Therefore, the trained neural network has optimized performances of bit error detection. Simulation results show that our proposed scheme can significantly improve the performance of the bit error detection and increase the endurance of NAND flash memory.


Author(s):  
Ting Cheng ◽  
Jianquan Jia ◽  
Lei Jin ◽  
Xinlei Jia ◽  
Shiyu Xia ◽  
...  

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