Abstract: Automated generation of certificates from a Certificate Authority through the use of an Enrollment Agent. Devices needing certificates generate the necessary keys and package public key information with other identifying information about the device and send this information to an Enrollment Agent. The Enrollment Agent takes this information and submits it on behalf of the device to a Certificate Authority, managing the interaction with the Certificate Authority on behalf of the device. The Certificate Authority signs the request, returning a certificate to the Enrollment Agent. The Enrollment Agent packages the certificate along with the other certificates needed to establish a chain of trust and returns these to the device. Certificates may be stored in the device in flash memory. The process is secure as long as the communications path between the devices and the Enrollment Agent is secure; a secure VPN or HTTPS: connection allows the devices and the Enrollment Agent to be in separate locations.

Abstract: Provisioning remote access points for use in a telecommunication network. A remote access point contains identity information established during manufacturing; this identity information may be in the nature of a digital certificate. The identity information is stored in the remote access point, and may be stored in a Trusted Platform Module if present. When the remote access node is powered up in unprovisioned state, outside the manufacturing environment, it attempts to establish an internet connection via a first wired interface, and queries a user for information representing the TCP/IP address of its controller via a second wired interface. Once an internet connection is present, and a TCP/IP address has been provided, the remote access point attempts to connect to the controller at that address. The controller may filter connection requests through a whitelist of approved remote access points. Once a connection is established, controller and access point exchange and verify each other's identities.

Abstract: A universal recipe editor for editing of semiconductor-manufacturing recipes. The universal recipe editor can read recipes from a wide variety of semiconductor-manufacturing machines from different manufacturers using recipe distributed object model (R-DOM) files. An R-DOM file is generated for each kind of recipe-file format to locate process parameters within proprietary recipe-file formats. The sequence of parameters in the R-DOM file matches the sequence in the recipe data file so that parameter may be mapped from recipe data files for display and editing. ASCII or binary recipe file formats are mapped from the recipe data file using R-DOM objects. Revision and authorng information is kept in a recipe information file for each recipe.

Abstract: A universal recipe editor is for off-line viewing and editing of semiconductor-manufacturing recipes. The universal recipe editor can read recipes from a wide variety of semiconductor-manufacturing machines from different manufacturers using recipe distributed object model (R-DOM) files. An R-DOM file is generated for each kind of recipe-file format to locate process parameters within proprietary recipe-file formats. The sequence of parameters in the R-DOM file matches the sequence in the recipe data file so that parameters may be mapped from recipe data files for display and editing. ASCII or binary recipe file formats are mapped from the recipe data file using R-DOM objects. Revision and authoring information is kept in a recipe information file for each recipe. Each line of the recipe data file can specify a different process parameter. Security or access rights for each parameter is added for each parameter by including security codes on each line in the R-DOM file.

Abstract: A universal recipe editor is for off-line viewing and editing of semiconductor-manufacturing recipes. The universal recipe editor can read recipes from a wide variety of semiconductor-manufacturing machines from different manufacturers using recipe distributed object model (R-DOM) files. An R-DOM file is generated for each kind of recipe-file format to locate process parameters within proprietary recipe-file formats. The sequence of parameters in the R-DOM file matches the sequence in the recipe data file so that parameters may be mapped from recipe data files for display and editing. ASCII or binary recipe file formats are mapped from the recipe data file using R-DOM objects. Revision and authoring information is kept in a recipe information file for each recipe. Each line of the recipe data file can specify a different process parameter. Security or access rights for each parameter is added for each parameter by including security codes on each line in the R-DOM file.

Abstract: A method and system are disclosed for generating a decision-tree classifier in parallel in a multi-processor system, from a training set of records. The method comprises the steps of: partitioning the records among the processors, each processor generating an attribute list for each attribute, and the processors cooperatively generating a decision tree by repeatedly partitioning the records using the attribute lists. For each node, each processor determines its best split test and, along with other processors, selects the best overall split for the records at that node. Preferably, the gini-index and class histograms are used in determining the best splits. Also, each processor builds a hash table using the attribute list of the split attribute and shares it with other processors. The hash tables are used for splitting the remaining attribute lists.

Abstract: A method and system are disclosed for generating a decision-tree classifier in parallel in a multi-processor system, from a training set of records. The method comprises the steps of: partitioning the records among the processors, each processor generating an attribute list for each attribute, and the processors cooperatively generating a decision tree by repeatedly partitioning the records using the attribute lists. For each node, each processor determines its best split test and, along with other processors, selects the best overall split for the records at that node. Preferably, the gini-index and class histograms are used in determining the best splits. Also, each processor builds a hash table using the attribute list of the split attribute and shares it with other processors. The hash tables are used for splitting the remaining attribute lists.

Abstract: A method and system are disclosed for generating a decision-tree classifier from a training set of records, independent of the system memory size. The method comprises the steps of: generating an attribute list for each attribute of the records, sorting the attribute lists for numeric attributes, and generating a decision tree by repeatedly partitioning the records using the attribute lists. For each node, split points are evaluated to determine the best split test for partitioning the records at the node. Preferably, a gini index and class histograms are used in determining the best splits. The gini index indicates how well a split point separates the records while the class histograms reflect the class distribution of the records at the node. Also, a hash table is built as the attribute list of the split attribute is divided among the child nodes, which is then used for splitting the remaining attribute lists of the node.

Abstract: A method and apparatus are disclosed for generating a decision tree classifier from a training set of records. The method comprises the steps of: pre-sorting the records based on each numeric record attribute, creating a decision tree breadth-first, and pruning the tree based on the MDL principle. Preferably, the pre-sorting includes generating a class list and attribute lists, and independently sorting the numeric attribute lists. The growing of the tree includes evaluating possible splitting criteria and selecting a splitting test for each leaf node, based on a splitting index, and updating the class list to reflect new leaf nodes. In a preferred embodiment, the splitting index is a gini index. The pruning preferably includes encoding the decision tree and splitting tests in an MDL-based code, and determining whether to convert a node into a leaf node, prune its child nodes, or leave the node intact, based on the code length of the node.