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    • 4. 发明授权
    • Structures and methods of high efficient bit conversion for multi-level cell non-volatile memories
    • 用于多级单元非易失性存储器的高效位转换的结构和方法
    • US08730723B2
    • 2014-05-20
    • US13417655
    • 2012-03-12
    • Lee Wang
    • Lee Wang
    • G11C16/04
    • G11C11/5642G11C16/06G11C16/26
    • Structures and methods of converting Multi-Level Cell (MLC) Non-Volatile Memory (NVM) into multi-bit information are disclosed. In MLC NVM system, multi-bit information stored in NVM cell is represented by the states of NVM cell threshold voltage levels. In this disclosure, “P” states of NVM cell threshold voltage levels are divided into “N” groups of threshold voltage levels. Each group contains “M” states of multiple threshold voltage levels of NVM cells, where P=N×M. The “M” states of NVM cell threshold voltage levels in each group are sensed and resolved by applying one correspondent gate voltage to the group. By applying “N” multiple gate voltages, the whole “P” states of NVM cell threshold voltage levels can be sensed and efficiently converted into storing bits in the MLC NVM cells.
    • 公开了将多级单元(MLC)非易失性存储器(NVM)转换为多位信息的结构和方法。 在MLC NVM系统中,存储在NVM单元中的多位信息由NVM单元阈值电压电平的状态表示。 在本公开中,NVM单元阈值电压电平的“P”状态被分为阈值电压电平的“N”组。 每组包含NVM单元的多个阈值电压电平的“M”状态,其中P = N×M。 通过向组中施加一个对应的栅极电压来感测和解析每组中的NVM单元阈值电压电平的“M”状态。 通过施加“N”个多个栅极电压,可以感测NVM单元阈值电压电平的整个“P”状态并有效地转换成MLC NVM单元中的存储位。
    • 5. 发明授权
    • Structures and methods to store information representable by a multiple-bit binary word in electrically erasable, programmable read-only memory (EEPROM)
    • 用于存储由电可擦除可编程只读存储器(EEPROM)中的多位二进制字表示的信息的结构和方法,
    • US08031524B2
    • 2011-10-04
    • US12392283
    • 2009-02-25
    • Lee Wang
    • Lee Wang
    • G11C16/04
    • G11C16/26
    • Innovative structures and methods to store information capable of being represented by an n-bit binary word in electrically erasable Programmable Read-Only memories (EEPROM) are disclosed. To program a state below the highest threshold voltage for an N-type Field Effect Transistor (NFET) based EEPROM, the stored charge in the floating gate for the highest threshold voltage is erased down to the desired threshold voltage level of the EEPROM by applying an appropriate voltage to the control gate and drain of the NFET. The erase-down uses drain-avalanche-hot hole injection (DAHHI) for the NFET memory device to achieve the precise threshold voltage desired for the NFET EEPROM device. The method takes advantage of the self-convergent mechanism from the DAHHI current in the device, when the device reaches a steady state. For a “READ” operation, a read voltage is applied to the control gate and the drain is connected by a current load to the positive voltage supply. Using the distinctive threshold voltage associated with the different stored charges, the output voltage from the drain is distinctively recognized and converted back to the original n-bit word. A similar method for a PFET EEPROM is also disclosed.
    • 公开了用于存储能够由电可擦除可编程只读存储器(EEPROM)中的n位二进制字表示的信息的创新结构和方法。 为了对基于N型场效应晶体管(NFET)的EEPROM进行低于最高阈值电压的状态,将用于最高阈值电压的浮置栅极中存储的电荷通过施加到EEPROM的方式被擦除到期望的阈值电压电平 适当的电压到NFET的控制栅极和漏极。 擦除使用漏极 - 雪崩 - 热空穴注入(DAHHI)作为NFET存储器件,以达到NFET EEPROM器件所需的精确阈值电压。 当器件达到稳定状态时,该方法利用了器件中DAHHI电流的自融合机制。 对于“READ”操作,将读取电压施加到控制栅极,并且漏极通过电流负载连接到正电压源。 使用与不同存储电荷相关联的独特阈值电压,来自漏极的输出电压被不同地识别并转换回原始的n位字。 还公开了类似的PFET EEPROM的方法。
    • 8. 发明授权
    • Keyword search volume seasonality forecasting engine
    • 关键词搜索量季节性预测引擎
    • US07676521B2
    • 2010-03-09
    • US11394089
    • 2006-03-31
    • Lee WangLi LiShuzhen NongYing Li
    • Lee WangLi LiShuzhen NongYing Li
    • G06F17/30
    • G06Q30/02G06Q10/04
    • A method and system are provided for forecasting keyword search volume. Keywords are categorized by concept and by the amount of data available for use in predicting future behavior. The keywords and/or the categories can also be categorized as seasonal or non-seasonal. A category level seasonal variation pattern can then be calculated based on keywords in the category that have sufficient historical data. A search volume can then be predicted for one or more keywords, with an appropriate calculation algorithm being selected based on the concept category, seasonal classification, and historical data available for the keywords.
    • 提供了一种用于预测关键字搜索量的方法和系统。 关键词按概念和可用于预测未来行为的数据量进行分类。 关键字和/或类别也可以分为季节性或非季节性。 然后可以基于具有足够历史数据的类别中的关键字来计算类别级季节变化模式。 然后可以针对一个或多个关键字预测搜索量,根据可用于关键字的概念类别,季节性分类和历史数据选择适当的计算算法。