Taiwan Semiconductor Manufacturing Comp. Ltd.

Chia-Tong Ho

Jong-I Mou

Po-Feng Tsai

Jo Fei Wang

Sunny Wu

Chin-Hsiang Lin

Jui-Long Chen

Hui-yun Chao

Tze-Liang Lee

Shih-Wei Wang

Ching-Sen Kuo

Hsiang-Fan Lee

Sheng-Wei Lee

Jo-Fei Wang

Yi-Jiun Lin

Kuo-Wei Chu

Jung-Chang Chen

Chia-Hsing Liao

A method and system for removing control action effects from inline measurement data for tool condition monitoring is disclosed. An exemplary method includes determining a control action effect that contributes to an inline measurement, wherein the inline measurement indicates a wafer characteristic of a wafer processed by a process tool; and evaluating the inline measurement without the control action effect contribution to determine a condition of the process tool.

Method and system for tool condition monitoring based on a simulated inline measurement

A system, a method, and a non-transitory computer readable storage medium for controlling an ion implanter are disclosed herein. The system includes a sample module and a control module. The sample module is configured to generate a summarized value from process data of the ion implanter, and the process data correspond to a control parameter. The control module is configured to tune a control parameter, and the control module performs an ion implantation by releasing tools of the ion implanter in accordance with the control parameter when the summarized value meets a predetermined stability requirement.

System and method for controlling ion implanter

The present disclosure provides various methods for tool condition monitoring, including systems for implementing such monitoring. An exemplary method includes receiving data associated with a process performed on wafers by an integrated circuit manufacturing process tool; and monitoring a condition of the integrated circuit manufacturing process tool using the data. The monitoring includes evaluating the data based on an abnormality identification criterion, an abnormality filtering criterion, and an abnormality threshold to determine whether the data meets an alarm threshold. The method may further include issuing an alarm when the data meets the alarm threshold.

Qualitative fault detection and classification system for tool condition monitoring and associated methods

The present disclosure provides various methods for tuning process parameters of a process tool, including systems for implementing such tuning. An exemplary method for tuning process parameters of a process tool such that the wafers processed by the process tool exhibit desired process monitor items includes defining behavior constraint criteria and sensitivity adjustment criteria; generating a set of possible tool tuning process parameter combinations using process monitor item data associated with wafers processed by the process tool, sensitivity data associated with a sensitivity of the process monitor items to each process parameter, the behavior constraint criteria, and the sensitivity adjustment criteria; generating a set of optimal tool tuning process parameter combinations from the set of possible tool tuning process parameter combinations; and configuring the process tool according to one of the optimal tool tuning process parameter combinations.

Systems and associated methods for tuning processing tools

A system and method for monitoring a process tool of an integrated circuit manufacturing system are disclosed. An exemplary method includes defining zones of an integrated circuit manufacturing process tool; grouping parameters of the integrated circuit manufacturing process tool based on the defined zones; and evaluating a condition of the integrated circuit manufacturing process tool based on the grouped parameters.

Integrated circuit manufacturing tool condition monitoring system and method

A method of automatically determining process parameters for processing equipment includes processing at least one first substrate in the processing equipment at a first time; and processing at least one second substrate in the processing equipment at a second time. The method includes collecting data on process monitors for the at least one first substrate; and the at least one second substrate. The method includes receiving the data by a multiple-input-multiple-output (MIMO) optimization system. The method includes revising a sensitivity matrix, by a MIMO optimizer, using the data and an adaptive-learning algorithm, wherein the adaptive-learning algorithm revises the sensitivity matrix based on a learning parameter which is related to a rate of change of the processing equipment over time. The method includes determining a set of process parameters for the processing equipment by the MIMO optimizer, wherein the MIMO optimizer uses the revised sensitivity matrix to determine the process parameters.

Adaptive and automatic determination of system parameters

A system and method for manufacturing semiconductor devices is disclosed. An embodiment comprises using desired device parameters to choose an initial manufacturing recipe. Once chosen, the initial manufacturing recipe may be modified by determining and applying an offset adjustment based on previous manufacturing to tune the recipes for the particular equipment to be utilized in the manufacturing process.

Auto device skew manufacturing

A MIMO optimizer is used to identify tunable process parameters for processing equipment.

System and method for data mining and feature tracking for fab-wide prediction and control are described. One embodiment is a system comprising a database for storing raw wafer manufacturing data; a data mining module for processing the raw wafer manufacturing data to select the best data therefrom in accordance with at least one of a plurality of knowledge-, statistic-, and effect-based processes; and a feature tracking module associated with the data mining module and comprising a self-learning model wherein a sensitivity of the self-learning model is dynamically tuned to meet real-time production circumstances, the feature tracking module receiving the selected data from the data mining module and generating prediction and control data therefrom; wherein the prediction and control data are used to control future processes in the wafer fabrication facility.

System and method for data mining and feature tracking for fab-wide prediction and control

A method for forming triple polysilicon split gate electrodes in a EEPROM flash memory array providing a first gate structure; blanket depositing a first polysilicon layer over the first gate structure; etching back the first polysilicon layer according to a first dry etching process; blanket depositing a second dielectric insulating layer over the first polysilicon layer; blanket depositing a second polysilicon layer over the second dielectric insulating layer; and, lithographically patterning and dry etching according to a respective third and fourth dry etching process through a thickness portion of the respective second and first polysilicon layers to respectively form third and second polysilicon gate electrodes.

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Taipei, Taiwan

Chia-Tong Ho