긍정신경과학의 이론 정립

학위논문(석사)아주대학교 일반대학원 :의학과,2012. 2- 국문요약 - 행복의 신경 생물학 - 긍정 신경과학의 이론 정립 ‘행복’은 인간에게 가장 근본적이고 중요한 삶의 체험이며, 동시에 생물학적으로는 특정한 뇌의 상태의 소산임에 틀림없다. 그러나 주관적인 체험으로서의 행복과 뇌의 작용을 설명하는 신경생물학 사이에는 아직 커다란 간격이 존재 한다. 그 이유는 먼저 행복이라는 개념이 너무 다양하고 주관적이며 사회 문화적 변인에 쉽게 영향을 받는 사적인 체험을 의미하는 것이며, 한편으로는 인간의 마음의 기전을 탐구하는 인지신경과학의 분야에서는 행복을 과학적 주제로 다룰 수 있는 포괄적 개념 도출이 이루어지지 않고 있기 때문이다. 본 연구에서는 ‘행복’을 쾌감이나 긍정적 정서의 일종으로 환원 분석하는 대신, ‘모든 인간은 행복을 추구하기 때문에 삶을 영위할 수 있다’라는 전제 하에 신경생물학적 설명을 시도하였다. 즉 ‘행복’을 ‘생존과 번성을 위한 긍정적 가치추구를 지향하는 일련의 정보처리 과정’이라는 인지과학적인 개념으로 변환하여, 인간의 주관적인 체험의 특성과 연계하여 구성요소들을 기술하였다. 그리고 긍정적인 가치를 추구하는 현상을 생물학적으로 설명할 수 있는 기전으로 Mesolimbic-cortical Reward P ⅰ athway, 특히 Dopaminergic Neuron의 전기생리학적 특성과 Nucleus Accumbens, 그리고 Prefrontal Cortex의 상호작용을 검토하여, Positive Value Processing Theory of Happiness(PVPTH)를 제시하였다. 그리고 5가지의 다축적 구성 요소들(H1: Value Exploration, H2: Value Attribution, H3: Value Acquisition, H4: Value Cosummation, H5: Re-entry Mechanism)을 소개하고, 행복과 관련된 인간의 주관적 체험의 설명을 시도하였다. PVPTH는 앞으로 방법론적 엄정성과 다양한 신경과학적 결과들을 설명할 수 있는 능력이 검증되어야 하는 매우 초보적이고 미성숙한 이론이지만, 이를 기반으로 하여 인간의 행복 추구의 명암을 검토해 보고, 참되고 지속적인 행복의 실현성을 연구주제로 하는 긍정신경과학(Positive Neuroscience)의 필요성을 강조하였다. 핵심어: 행복, 뇌, 가치, 보상체계, 도파민, 긍정신경과학 ⅱMaste

Characterization of Incremental Step Pulse Programming (ISPP) for 3D NAND Flash Memory

MasterThe demand of memory devices is increasing because electronics recently require more data processing than before. Semiconductor technology have been growing by Moore’s law. In past, central processing unit (CPU) was only embedded on computer but application units (APs) like CPUs are built into mobile and Internet of Things (IOT) devices to communicate between each other in addition to their original features. Despite the smaller size of AP than CPU, AP requires to store and process, so the memory system meets the demand. Storage is the element of computer to write and preserve non-volatile data and dynamic random access memory (DRAM) is to store volatile data, and to help AP and CPU to access data or address where data is written. In the storages so called non-volatile memory, NAND Flash memory device is in the spotlight because of their high degree of integration. NAND Flash memory has the merits: faster speed and higher density than the conventional storage, that is, hard disk drive (HDD). Since transistor concept was invented, semiconductor companies or manufacturers have focused on optimization of speed, power consumption, density and cost. Because NAND Flash memory is closely related to density and cost, 3D NAND Flash memory structure has been proposed as a game changer to increase density to overcome the scaling difficulty of 2D structure. Furthermore, as the technology of storing multi-bit has been proposed, it is possible to make NAND Flash memory dense and to reduce the cost. This technique is called Multi-Level Cell (MLC) technique. The width of threshold voltage Vth distribution of NAND Flash memory cells widens, when the binary information is written in the NAND Flash memory by using MLC technique. The widening of Vth distribution width, underprogram and overprogram problems are starting to emerge in MLC. Therefore, we use the scheme that is called incremental step pulse programming (ISPP) to make narrow the width of Vth distribution. In this thesis, we simulated the causal candidate to the variation of ISPP slope and measured Vth with respective to different Vstep, WL location for chip and wafer during ISPP and inspected whether ISPP slope is ideally 1 V/V or not, and if not, analyze why it is not

Improved ISPP scheme for narrow threshold voltage distribution in 3-D NAND flash memory

Three-dimensional NAND flash technology exhibits a trend of increasing bit density. The narrow threshold voltage (Vth) distribution of each program state in a chip is important for increasing the number of bits in a multilevel cell (MLC) technique. An abnormal program cell (APC), which is an excessively programmed cell whose Vth overlaps with the next program state, increases the Vth distribution width (Wv). The wide Vth distribution makes it difficult to distinguish the data stored in each cell and causes data errors. In this study, an improved incremental step pulse programming (ISPP) method to narrow the Vth distribution has been proposed. As the programming step voltage (Vstep) decreases immediately before the target cells pass the nth program verify level (PVn), the difference between Vth and PVn decreases, causing a reduction in the number of APCs. Therefore, in the improved ISPP, the Vstep is selectively reduced at the target ISPP steps at which most cells are predicted to be programmed in the next ISPP step for each program state. As a result, the Wv of the improved scheme decreases compared to the conventional scheme with the minimum increase in the total number of program pulses. Larger bit density is feasible by applying improved ISPP, resulting in high-capacity NAND flash memory.11Nsciescopu

Holistic Optimization of Trap Distribution for Performance/Reliability in 3-D NAND Flash Using Machine Learning

A machine learning (ML) method was used to optimize the trap distribution of the charge trap nitride (CTN) to simultaneously improve its performance/reliability (P/R) characteristics, which are tradeoffs in 3-D NAND flash memories. Using an artificial neural network (ANN), we modeled the relationship between trap distributions and P/R characteristics. The ANN was trained using a large experimentally calibrated technology computer-aided design (TCAD) simulation dataset. The gradient descent method was adapted to optimize the trap distribution, achieving the best P/R characteristics based on the well trained ANN. Eventually, we found the best trap profile distributed in both space and energy. In particular, the energetic trap distribution had a larger impact on the P/R characteristics than that of the spatial trap distribution. Furthermore, in terms of the P/R characteristics, it was generally preferable to increase all inputs of the energetic trap distribution. However, the acceptor-like trap energy level (ETA) and its standard deviation (sigma EA) caused a tradeoff between P/R characteristics; therefore, ML was used to determine their optimal points. The proposed ML method allows the optimization of trap distribution to obtain the best P/R characteristics rapidly and quantitatively. Our findings could be used as a guideline for determining the physical properties of CTN in 3-D NAND flash cells.11Ysciescopu