会员体验
专利管家(专利管理)
工作空间(专利管理)
风险监控(情报监控)
数据分析(专利分析)
侵权分析(诉讼无效)
联系我们
交流群
官方交流:
QQ群: 891211   
微信请扫码    >>>
现在联系顾问~
热词
    • 4. 发明授权
    • Method for predicting performance of microelectronic device based on
electrical parameter test data using computer model
    • 基于电参数测试数据使用计算机模型预测微电子器件性能的方法
    • US6028994A
    • 2000-02-22
    • US73619
    • 1998-05-06
    • Yeng-Kaung PengChern-Jiann LeeSiu-May Ho
    • Yeng-Kaung PengChern-Jiann LeeSiu-May Ho
    • G01R31/30H01L21/66G06F9/455
    • H01L22/20G01R31/30
    • Electrical parameter testing and performance testing are performed on a plurality of microelectronic devices to obtain parametric values and performance values respectively. The parametric values are applied as inputs to a computer program such as a back propagation neural network engine which generates a performance prediction model by self-learning that implements a function relating the performance values to the parametric values. The model is used to predict the performance of devices being fabricated by performing electrical parameter testing on these devices and applying the resulting parametric values to the model as inputs to produce predicted performance values as outputs. The model can be configured to produce predicted performance values as percentages of devices having speed or other parameters in predetermined respective ranges. The model can be further configured to produce predicted performance values as percentages of devices having different types of defects. The model can be improved by self-learning using additional test values. The model can also be used to identify parameters which result in low performance and improve devices being fabricated by adjusting the corresponding process parameters.
    • 在多个微电子器件上执行电参数测试和性能测试,以分别获得参数值和性能值。 将参数值作为输入应用于计算机程序,例如反向传播神经网络引擎,其通过自学习产生性能预测模型,该模型实现与性能值相关的参数值。 该模型用于通过对这些器件执行电气参数测试来预测正在制造的器件的性能,并将所得到的参数值作为输入应用到模型中,以产生预测的性能值作为输出。 该模型可以被配置为产生预测的性能值,作为具有预定相应范围内的速度或其他参数的设备的百分比。 该模型可进一步配置为产生具有不同类型缺陷的设备的百分比的预测性能值。 该模型可以通过使用附加测试值的自学习来改进。 该模型还可用于识别导致低性能的参数,并通过调整相应的过程参数来改进制造的器件。
    • 5. 发明授权
    • Method and apparatus for pattern recognition of wafer test bins
    • 晶圆测试箱的模式识别方法和装置
    • US5787190A
    • 1998-07-28
    • US884316
    • 1997-06-27
    • Yeng-Kaung PengSiu-May HoYing Shiau
    • Yeng-Kaung PengSiu-May HoYing Shiau
    • H01L21/66
    • H01L22/20
    • An automated system and procedure processes wafer test bin data of semiconductor wafers to formulate a fault pattern at statistically significant levels. A processor such as a neural engine or neural network collects wafer test bin results to generate a N/N wafer map to be correlated with wafer maps produced from a wafer electrical test, a wafer level reliability test, and an in-line defect analysis. A N/N wafer map generated by the processor is cross-checked with a wafer map generated from another semiconductor tester to formulate possible overlap fault patterns. The confirmed fault patterns are further analyzed by performing failure analysis to find the root cause of fault patterns. A report containing fault patterns and the root cause for fault patterns is sent back to a fab for making adjustment to the fabrication process to increase the overall yield of the future batch of semiconductor wafers. The report is also stored in a pattern database to serve as a library for future reference of previously recognized fault patterns, thereby to bypass the need to perform a failure analysis for matching fault patterns.
    • 自动化系统和程序处理半导体晶片的晶圆测试箱数据,以制定统计学显着水平的故障模式。 诸如神经引擎或神经网络的处理器收集晶片测试箱结果以产生N / N晶片图,以与从晶片电测试,晶片级可靠性测试和在线缺陷分析产生的晶片图相关联。 由处理器产生的N / N晶片图与从另一半导体测试仪生成的晶片图进行交叉检查,以制定可能的重叠故障模式。 通过执行故障分析,找出故障模式的根本原因进一步分析确认的故障模式。 包含故障模式和故障模式的根本原因的报告被发送回制造厂,以调整制造工艺以提高未来批次的半导体晶片的总体产量。 报告也存储在模式数据库中,用作库以供以前识别的故障模式的参考,从而绕过对故障模式匹配进行故障分析的需要。
    • 6. 发明授权
    • Apparatus and methods for characterizing floating body effects in SOI devices
    • 用于表征SOI器件中浮体效应的装置和方法
    • US06774395B1
    • 2004-08-10
    • US10345007
    • 2003-01-15
    • Hung-Jen LinW Eugene HillMario M. PelellaChern-Jann LeeSrikanth SundararajanSiu May Ho
    • Hung-Jen LinW Eugene HillMario M. PelellaChern-Jann LeeSrikanth SundararajanSiu May Ho
    • H01L2358
    • H01L22/34
    • Methods are described for characterizing floating body delay effects in SOI wafers comprising providing a pulse edge to a floating body and a tied body chain in the wafer, storing tied body chain data according to one or more of the floating body devices, and characterizing the floating body delay effects according to the stored tied body chain data. Test apparatus are also described comprising a floating body chain including a plurality of series connected floating body inverters or NAND gates fabricated in the wafer and a tied body chain comprising a plurality of series connected tied body devices to in the wafer. Storage devices are coupled with the tied body devices and with one or more of the floating body devices and operate to store tied body chain data from the tied body devices according to one or more signals from floating body chain.
    • 描述了用于表征SOI晶片中的浮体延迟效应的方法,包括向晶片中的浮体和绑定的主体链提供脉冲边缘,根据一个或多个浮体装置存储绑定的身体链数据,并表征浮动 根据存储的绑定的身体链数据的身体延迟效应。 还描述了测试装置,其包括浮体体链,其包括在晶片中制造的多个串联连接的浮体反相器或NAND门,以及包括多个串联连接的绑定体装置的晶片的绑定体链。 存储装置与绑定的身体装置和一个或多个浮体装置相耦合,并根据来自浮体的一个或多个信号,操作以从捆绑的身体装置中存储绑定的身体链数据。
    • 7. 发明授权
    • Real-time in-line defect disposition and yield forecasting system
    • 实时在线缺陷处置和收益预测系统
    • US5598341A
    • 1997-01-28
    • US401490
    • 1995-03-10
    • Zhi-Min LingThao VoSiu-May HoYing ShiauYeng-Kaung PengYung-Tao Lin
    • Zhi-Min LingThao VoSiu-May HoYing ShiauYeng-Kaung PengYung-Tao Lin
    • G01N21/95G03F7/20H01L21/00H01L21/66G06F19/00
    • H01L21/67288G01N21/9501G03F7/705G03F7/7065H01L21/67276H01L22/20
    • A real-time in-line defect disposition and yield forecasting system for a semiconductor wafer having layer containing devices includes an in-line fabrication inspection tool, a design review station, and a yield management station. The in-line fabrication inspection tool inspects at least two layers of the semiconductor wafer and produces first information including particle size, particle location and number of particles introduced therein for each of these layers. The design review station inspects the layers of the semiconductor wafer and produces second information including layouts of each of the layers. The yield management station is operatively connected to the in-line fabrication inspection tool and to the design review station. The yield management station retrieves the first information and the second information from the in-line fabrication inspection tool and from the design review station. The yield management station determines at least one of a number of killer defects for the devices in each of the layers or a defect sensitive area index for each of the layers using the first and second information. The yield management station also determines a priority for analyzing each of the at least two layers responsive to at least one of the number of killer defects and the defect sensitive area index for each of the layers.
    • 具有层包含装置的半导体晶片的实时在线缺陷布置和产量预测系统包括在线制造检查工具,设计审查站和产量管理站。 在线制造检查工具检查半导体晶片的至少两层,并且产生包括其中为每个这些层引入的颗粒尺寸,颗粒位置和颗粒数量的第一信息。 设计检查站检查半导体晶片的层,并产生包括每个层的布局的第二信息。 产量管理站可操作地连接到在线制造检查工具和设计审查站。 产量管理站从设计检查站检索来自在线制造检验工具的第一信息和第二信息。 收益管理站使用第一和第二信息确定每个层中的设备的多个杀手缺陷中的至少一个或每个层的缺陷敏感区域索引。 收益管理站还根据每个层的凶手缺陷数量和缺陷敏感区域索引中的至少一个来确定分析至少两个层中的每一个的优先级。