公开(公告)号：US20110227194A1

公开(公告)日：2011-09-22

申请号：US13052262

申请日：2011-03-21

申请人： Simon Jeannot ,  Pascal Tannhof

发明人： Simon Jeannot ,  Pascal Tannhof

IPC分类号： H01L29/92 ,  H01L21/02

CPC分类号： H01L23/5223 ,  H01L28/60 ,  H01L2924/0002 ,  H01L2924/00

摘要： A method for forming a capacitive structure in a metal level of an interconnection stack including a succession of metal levels and of via levels, including the steps of: forming, in the metal level, at least one conductive track in which a trench is defined; conformally forming an insulating layer on the structure; forming, in the trench, a conductive material; and planarizing the structure.

摘要翻译： 一种用于在包括一系列金属水平和通孔级别的互连堆叠的金属层中形成电容结构的方法，包括以下步骤：在金属层面形成至少一个其中限定沟槽的导电轨道; 在结构上保形地形成绝缘层; 在沟槽中形成导电材料; 并平坦化结构。

公开(公告)号：US08609530B2

公开(公告)日：2013-12-17

申请号：US13052262

申请日：2011-03-21

申请人： Simon Jeannot ,  Pascal Tannhof

发明人： Simon Jeannot ,  Pascal Tannhof

IPC分类号： H01L21/4763

CPC分类号： H01L23/5223 ,  H01L28/60 ,  H01L2924/0002 ,  H01L2924/00

摘要： A method for forming a capacitive structure in a metal level of an interconnection stack including a succession of metal levels and of via levels, including the steps of: forming, in the metal level, at least one conductive track in which a trench is defined; conformally forming an insulating layer on the structure; forming, in the trench, a conductive material; and planarizing the structure.

摘要翻译： 一种用于在包括一系列金属水平和通孔级别的互连堆叠的金属层中形成电容结构的方法，包括以下步骤：在金属层面形成至少一个其中限定沟槽的导电轨道; 在结构上保形地形成绝缘层; 在沟槽中形成导电材料; 并平坦化结构。

公开(公告)号：US07254565B2

公开(公告)日：2007-08-07

申请号：US10137969

申请日：2002-05-03

申请人： Ghislain Imbert De Tremiolles ,  Pascal Tannhof

发明人： Ghislain Imbert De Tremiolles ,  Pascal Tannhof

IPC分类号： G06E1/00 ,  G06E3/00 ,  G06F15/18 ,  G06G7/00 ,  G06N3/00 ,  G06N3/08 ,  G06N3/04

CPC分类号： G06K9/6276 ,  G06N3/063

摘要： An improved Artificial Neural Network (ANN) is disclosed that comprises a conventional ANN, a database block, and a compare and update circuit. The conventional ANN is formed by a plurality of neurons, each neuron having a prototype memory dedicated to store a prototype and a distance evaluator to evaluate the distance between the input pattern presented to the ANN and the prototype stored therein. The database block has: all the prototypes arranged in slices, each slice being capable to store up to a maximum number of prototypes; the input patterns or queries to be presented to the ANN; and the distances resulting of the evaluation performed during the recognition/classification phase. The compare and update circuit compares the distance with the distance previously found for the same input pattern updates or not the distance previously stored.

摘要翻译： 公开了一种改进的人造神经网络（ANN），其包括常规ANN，数据库块以及比较和更新电路。 常规ANN由多个神经元形成，每个神经元具有专用于存储原型的原型存储器和距离评估器，以评估呈现给ANN的输入模式与存储在其中的原型之间的距离。 数据库块具有：所有原型以切片排列，每个切片能够存储最多数量的原型; 要呈现给ANN的输入模式或查询; 以及在识别/分类阶段期间进行评估的距离。 比较和更新电路将距离与先前发现的相同输入模式更新的距离进行比较，或将之前存储的距离进行比较。

公开(公告)号：US20070150621A1

公开(公告)日：2007-06-28

申请号：US11682547

申请日：2007-03-06

申请人： Kerry Kravec ,  Ali Saidi ,  Jan Slyfield ,  Pascal Tannhof

发明人： Kerry Kravec ,  Ali Saidi ,  Jan Slyfield ,  Pascal Tannhof

IPC分类号： G06F3/00

CPC分类号： G06K9/6202 ,  G06K9/00986

摘要： A parallel pattern detection engine (PPDE) comprise multiple processing units (PUs) customized to do various modes of pattern recognition. The PUs are loaded with different patterns and the input data to be matched is provided to the PUs in parallel. Each pattern has an Opcode that defines what action to take when a particular data in the input data stream either matches or does not match the corresponding data being compared during a clock cycle. Each of the PUs communicate selected information so that PUs may be cascaded to enable longer patterns to be matched or to allow more patterns to be processed in parallel for a particular input data stream.

公开(公告)号：US20050154916A1

公开(公告)日：2005-07-14

申请号：US10756904

申请日：2004-01-14

申请人： Marc Boulanger ,  Clark Jeffries ,  C. Kinard ,  Kerry Kravec ,  Ravinder Sabhikhi ,  Ali Saidi ,  Jan Slyfield ,  Pascal Tannhof

发明人： Marc Boulanger ,  Clark Jeffries ,  C. Kinard ,  Kerry Kravec ,  Ravinder Sabhikhi ,  Ali Saidi ,  Jan Slyfield ,  Pascal Tannhof

IPC分类号： H04L9/00 ,  H04L12/24 ,  H04L29/06

CPC分类号： H04L63/1416 ,  H04L63/1441

摘要： An intrusion detection system (IDS) comprises a network processor (NP) coupled to a memory unit for storing programs and data. The NP is also coupled to one or more parallel pattern detection engines (PPDE) which provide high speed parallel detection of patterns in an input data stream. Each PPDE comprises many processing units (PUs) each designed to store intrusion signatures as a sequence of data with selected operation codes. The PUs have configuration registers for selecting modes of pattern recognition. Each PU compares a byte at each clock cycle. If a sequence of bytes from the input pattern match a stored pattern, the identification of the PU detecting the pattern is outputted with any applicable comparison data. By storing intrusion signatures in many parallel PUs, the IDS can process network data at the NP processing speed. PUs may be cascaded to increase intrusion coverage or to detect long intrusion signatures.

摘要翻译： 入侵检测系统（IDS）包括耦合到用于存储程序和数据的存储器单元的网络处理器（NP）。 NP还耦合到一个或多个并行模式检测引擎（PPDE），其提供对输入数据流中的模式的高速并行检测。 每个PPDE包括许多处理单元（PU），每个处理单元被设计为将入侵签名存储为具有所选操作码的数据序列。 PU具有用于选择模式识别模式的配置寄存器。 每个PU在每个时钟周期比较一个字节。 如果来自输入模式的字节序列与存储的模式匹配，则用任何适用的比较数据输出检测模式的PU的识别。 通过在多个并行PU中存储入侵签名，IDS可以以NP处理速度处理网络数据。 PU可以级联以增加入侵覆盖或检测长入侵签名。

公开(公告)号：US06347309B1

公开(公告)日：2002-02-12

申请号：US09223478

申请日：1998-12-30

申请人： Ghislain Imbert De Tremiolles ,  Pascal Tannhof

发明人： Ghislain Imbert De Tremiolles ,  Pascal Tannhof

IPC分类号： G06N306

CPC分类号： G06K9/6271 ,  G06N3/063

摘要： The improved neural network of the present invention results from the combination of a dedicated logic block with a conventional neural network based upon a mapping of the input space usually employed to classify an input data by computing the distance between said input data and prototypes memorized therein. The improved neural network is able to classify an input data, for instance, represented by a vector A even when some of its components are noisy or unknown during either the learning or the recognition phase. To that end, influence fields of various and different shapes are created for each neuron of the conventional neural network. The logic block transforms at least some of the n components (A1, . . . , An) of the input vector A into the m components (V1, . . . , Vm) of a network input vector V according to a linear or non-linear transform function F. In turn, vector V is applied as the input data to said conventional neural network. The transform function F is such that certain components of vector V are not modified, e.g. Vk=Aj, while other components are transformed as mentioned above, e.g. Vi=Fi(A1, . . . , An). In addition, one (or more) component of vector V can be used to compensate an offset that is present in the distance evaluation of vector V. Because, the logic block is placed in front of the said conventional neural network any modification thereof is avoided.

摘要翻译： 本发明的改进的神经网络是基于通常用于通过计算所述输入数据与其中存储的原型之间的距离来对输入数据进行分类的输入空间的映射，将专用逻辑块与传统神经网络的组合。 改进的神经网络能够对例如由向量A表示的输入数据进行分类，即使在学习或识别阶段期间，其一些组件是噪声或未知的。 为此，为传统神经网络的每个神经元创建各种不同形状的影响场。 逻辑块根据线性或非线性将输入矢量A的n个分量（A1，...，An）中的至少一些变换成网络输入矢量V的m个分量（V1，...，Vm） 然后将矢量V作为输入数据施加到所述常规神经网络。 变换函数F使得向量V的某些分量不被修改，例如， Vk = Aj，而其它组分如上所述被转化，例如。 Vi = Fi（A1，...，An）。 另外，矢量V的一个（或多个）分量可以用于补偿矢量V的距离评估中存在的偏移。因为逻辑块被放置在所述传统神经网络的前面，所以避免了其任何修改 。

公开(公告)号：US07734117B2

公开(公告)日：2010-06-08

申请号：US12021511

申请日：2008-01-29

申请人： Pascal Tannhof ,  Ghislain I De Tremiolles

发明人： Pascal Tannhof ,  Ghislain I De Tremiolles

IPC分类号： G06K9/32

CPC分类号： G06T3/4046

摘要： An artificial neural network (ANN) based system that is adapted to process an input pattern to generate an output pattern related thereto having a different number of components than the input pattern. The system (26) is comprised of an ANN (27) and a memory (28), such as a DRAM memory, that are serially connected. The input pattern (23) is applied to a processor (22), where it can be processed or not (the most general case), before it is applied to the ANN and stored therein as a prototype (if learned). A category is associated with each stored prototype. The processor computes the coefficients that allow the determination of the estimated values of the output pattern, these coefficients are the components of a so-called intermediate pattern (24). Assuming the ANN has already learned a number of input patterns, when a new input pattern is presented to the ANN in the recognition phase, the category of the closest prototype is output therefrom and is used as a pointer to the memory. In turn, the memory outputs the corresponding intermediate pattern. The input pattern and the intermediate pattern are applied to the processor to construct the output pattern (25) using the coefficients. Typically, the input pattern is a block of pixels in the field of scaling images.

摘要翻译： 一种基于人造神经网络（ANN）的系统，其适于处理输入模式以产生与其相关的输出模式，该输出模式具有与输入模式不同数量的分量。 系统（26）由串联连接的ANN（27）和存储器（28）（诸如DRAM存储器）组成。 将输入模式（23）应用于处理器（22），在处理器（22）被应用于ANN并作为原型存储（如果被学习）之前）处理器（22），其可被处理（最常见的情况））。 类别与每个存储的原型相关联。 处理器计算允许确定输出图案的估计值的系数，这些系数是所谓的中间图案的分量（24）。 假设ANN已经学习了许多输入模式，当在识别阶段向ANN呈现新的输入模式时，最近的原型的类别从其输出并被用作指向存储器的指针。 反过来，存储器输出相应的中间模式。 将输入图案和中间图案应用于处理器，以使用系数构造输出图案（25）。 通常，输入图案是缩放图像领域的像素块。

公开(公告)号：US07352918B2

公开(公告)日：2008-04-01

申请号：US10321166

申请日：2002-12-17

申请人： Pascal Tannhof ,  Ghislain Imbert De Tremiolles

发明人： Pascal Tannhof ,  Ghislain Imbert De Tremiolles

IPC分类号： G06K9/32

CPC分类号： G06T3/4046

摘要： An artificial neural network (ANN) based system that is adapted to process an input pattern to generate an output pattern related thereto having a different number of components than the input pattern. The system (26) is comprised of an ANN (27) and a memory (28), such as a DRAM memory, that are serially connected. The input pattern (23) is applied to a processor (22), where it can be processed or not (the most general case), before it is applied to the ANN and stored therein as a prototype (if learned). A category is associated with each stored prototype. The processor computes the coefficients that allow the determination of the estimated values of the output pattern, these coefficients are the components of a so-called intermediate pattern (24). Assuming the ANN has already learned a number of input patterns, when a new input pattern is presented to the ANN in the recognition phase, the category of the closest prototype is output therefrom and is used as a pointer to the memory. In turn, the memory outputs the corresponding intermediate pattern. The input pattern and the intermediate pattern are applied to the processor to construct the output pattern (25) using the coefficients. Typically, the input pattern is a block of pixels in the field of scaling images.

摘要翻译： 一种基于人造神经网络（ANN）的系统，其适于处理输入模式以产生与其相关的输出模式，该输出模式具有与输入模式不同数量的分量。 系统（26）由串联连接的ANN（27）和存储器（28）（诸如DRAM存储器）组成。 将输入模式（23）应用于处理器（22），在处理器（22）被应用于ANN并作为原型存储（如果被学习）之前）处理器（22），其可被处理（最常见的情况））。 类别与每个存储的原型相关联。 处理器计算允许确定输出图案的估计值的系数，这些系数是所谓的中间图案的分量（24）。 假设ANN已经学习了许多输入模式，当在识别阶段向ANN呈现新的输入模式时，最近的原型的类别从其输出并被用作指向存储器的指针。 反过来，存储器输出相应的中间模式。 将输入图案和中间图案应用于处理器，以使用系数构造输出图案（25）。 通常，输入图案是缩放图像领域的像素块。

公开(公告)号：US5717832A

公开(公告)日：1998-02-10

申请号：US488443

申请日：1995-06-07

申请人： Andre Steimle ,  Pascal Tannhof ,  Guy Paillet

发明人： Andre Steimle ,  Pascal Tannhof ,  Guy Paillet

IPC分类号： G06F15/18 ,  G06N3/00 ,  G06N3/04 ,  G06N3/06 ,  G06N3/063 ,  G06E1/00

CPC分类号： G06K9/00986 ,  G06K9/6271 ,  G06N3/063

摘要： A base neural semiconductor chip (10) including a neural network or unit (11(#)). The neural network (11(#)) has a plurality of neuron circuits fed by different buses transporting data such as the input vector data, set-up parameters, and control signals. Each neuron circuit (11) includes logic for generating local result signals of the "fire" type (F) and a local output signal (NOUT) of the distance or category type on respective buses (NR-BUS, NOUT-BUS). An OR circuit (12) performs an OR function for all corresponding local result and output signals to generate respective first global result (R*) and output (OUT*) signals on respective buses (R*-BUS, OUT*-BUS) that are merged in an on-chip common communication bus (COM*-BUS) shared by all neuron circuits of the chip. In a multi-chip network, an additional OR function is performed between all corresponding first global result and output signals (which are intermediate signals) to generate second global result (R**) and output (OUT**) signals, preferably by dotting onto an off-chip common communication bus (COM**-BUS) in the chip's driver block (19). This latter bus is shared by all the base neural network chips that are connected to it in order to incorporate a neural network of the desired size. In the chip, a multiplexer (21) may select either the intermediate output or the global output signal to be fed back to all neuron circuits of the neural network, depending on whether the chip is used in a single or multi-chip environment via a feed-back bus (OR-BUS). The feedback signal is the result of a collective processing of all the local output signals.

摘要翻译： 一种包括神经网络或单元（11（＃））的基本神经半导体芯片（10）。 神经网络（11（＃））具有由不同总线馈送的多个神经元电路，其传送诸如输入矢量数据，设置参数和控制信号的数据。 每个神经元电路（11）包括用于在相应总线（NR-BUS，NOUT-BUS）上产生“火”类型（F）的本地结果信号和距离或类别类型的本地输出信号（NOUT）的逻辑。 OR电路（12）对所有对应的本地结果和输出信号执行OR功能，以在相应总线（R * -BUS，OUT * -BUS）上产生相应的第一全局结果（R *）和输出（OUT *）信号， 被合并在由芯片的所有神经元电路共享的片上公共通信总线（COM * -BUS）中。 在多芯片网络中，在所有对应的第一全局结果和输出信号（它们是中间信号）之间执行附加OR功能，以产生第二全局结果（R **）和输出（OUT **）信号，优选地通过点划线 在芯片的驱动器块（19）中的片外公共通信总线（COM ** - BUS）上。 该后一个总线由连接到它的所有基本神经网络芯片共享以便并入所需大小的神经网络。 在芯片中，多路复用器（21）可以选择要反馈给神经网络的所有神经元电路的中间输出或全局输出信号，这取决于芯片是经由单芯片还是多芯片环境使用 反馈总线（OR-BUS）。 反馈信号是对所有局部输出信号的集中处理的结果。

公开(公告)号：US5710869A

公开(公告)日：1998-01-20

申请号：US485337

申请日：1995-06-07

申请人： Catherine Godefroy ,  Andre Steimle ,  Pascal Tannhof ,  Guy Paillet

发明人： Catherine Godefroy ,  Andre Steimle ,  Pascal Tannhof ,  Guy Paillet

IPC分类号： G06F15/18 ,  G06N3/00 ,  G06N3/04 ,  G06N3/06 ,  G06N3/063 ,  G06F15/00

CPC分类号： G06N3/063

摘要： Each daisy chain circuit is serially connected to the two adjacent neuron circuits, so that all the neuron circuits form a chain. The daisy chain circuit distinguishes between the two possible states of the neuron circuit (engaged or free) and identifies the first free "or ready to learn" neuron circuit in the chain, based on the respective values of the input (DCI) and output (DCO) signals of the daisy chain circuit. The ready to learn neuron circuit is the only neuron circuit of the neural network having daisy chain input and output signals complementary to each other. The daisy chain circuit includes a 1-bit register (601) controlled by a store enable signal (ST) which is active at initialization or, during the learning phase when a new neuron circuit is engaged. At initialization, all the Daisy registers of the chain are forced to a first logic value. The DCI input of the first daisy chain circuit in the chain is connected to a second logic value, such that after initialization, it is the ready to learn neuron circuit. In the learning phase, the ready to learn neuron's 1-bit daisy register contents are set to the second logic value by the store enable signal, it is said "engaged". As neurons are engaged, each subsequent neuron circuit in the chain then becomes the next ready to learn neuron circuit.

摘要翻译： 每个菊花链电路串联连接到两个相邻的神经元电路，使得所有的神经元电路形成链。 菊花链电路基于输入（DCI）和输出（DCI）的相应值来区分神经元电路的两种可能状态（被接合或自由）并且识别链中的第一个“准备学习”神经元电路 DCO）信号。 准备学习神经元电路是具有菊花链输入和输出信号彼此互补的神经网络的唯一神经元电路。 菊花链电路包括由初始化时有效的存储使能信号（ST）控制的1位寄存器（601），或者在新的神经元电路被接合时的学习阶段。 在初始化时，链的所有Daisy寄存器都被强制为第一个逻辑值。 链中第一个菊花链电路的DCI输入连接到第二个逻辑值，这样在初始化之后就可以学习神经元电路了。 在学习阶段，准备学习神经元的1位菊花寄存器内容通过存储使能信号设置为第二个逻辑值，它被称为“被接合”。 随着神经元的啮合，链中随后的每个神经元电路就成为下一个准备学习神经元电路的准备。