Taiwan Semiconductor Manufacturing Comp. Ltd.

Tsinghua University

Po-Feng Tsai

Jong-I Mou

Jo Fei Wang

Chia-Tong Ho

Sunny Wu

Andy Tsen

Chin-Hsiang Lin

Chun-Jung Huang

Kuang Huan Hsu

Jin-Ning Sung

Li-Hsin Chu

Yung-Lin Hsu

Henry Peng

Tsung Wei Chen

Yen-Di Tsen

Tze-Liang Lee

Jen-Ti Wang

Jui-Long Chen

Hui-yun Chao

Sheng-Wei Lee

Ming-Yu Fan

Jo-Fei Wang

Shi-Shang Jang

Jung-Chang Chen

Yen-Wei Cheng

Jill Wang

Chia-Hsing Liao

An-sheng Chung

The present disclosure describes a system and a method for an ion implantation (IMP) process. The system includes an ion implanter configured to scan an ion beam over a target for a range of angles, a tilting mechanism configured to support and tilt the target, an ion-collecting device configured to collect a distribution and a number of ejected ions from the ion beam scan over the target, and a control unit configured to adjust a tilt angle based on a correction angle determined based on the distribution and number of ejected ions.

Method for an ion implantation process employing an ion-collecting device that collects a distribution of ejected ions from a target to correct a tilt angle of the target

A method for monitoring a transport vehicle is provided. The method includes the operations as follows. A transport vehicle is scanned by a monitor during the transport vehicle is operated on a rail to acquire a vehicle pattern of the transport vehicle. The vehicle pattern of the transport vehicle is analyzed. An abnormal transport vehicle is determined based on the vehicle pattern. The monitor is placed nearby the rail. A method for transport vehicle maintenance is also provided.

Method for monitoring transport vehicle and maintenance thereof

Method for ion implantation that adjusts a targets tilt angle based on a distribution of ejected ions from a target

The present disclosure describes a system and a method for a ion implantation (IMP) process. The system includes an ion implanter configured to scan an ion beam over a target for a range of angles, a tilting mechanism configured to support and tilt the target, an ion-collecting device configured to collect a distribution and a number of ejected ions from the ion beam scan over the target, and a control unit configured to adjust a tilt angle based on a correction angle determined based on the distribution and number of ejected ions.

Method for ion implantation that adjusts a target's tilt angle based on a distribution of ejected ions from a target

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 method of extending advanced process control (APC) models includes constructing an APC model table including APC model parameters of a plurality of products and a plurality of work stations. The APC model table includes empty cells and cells filled with existing APC model parameters. Average APC model parameters of the existing APC model parameters are calculated, and filled into the empty cells as initial values. An iterative calculation is performed to update the empty cells with updated values.

APC model extension using existing APC models

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

In accordance with an embodiment, a method for exception handling comprises accessing an exception type for an exception, filtering historical data based on at least one defined criterion to provide a data train comprising data sets, assigning a weight to each data set, and providing a current control parameter. The data sets each comprise a historical condition and a historical control parameter, and the weight assigned to each data set is based on each historical condition. The current control parameter is provided using the weight and the historical control parameter for each data set.

Processing exception handling

System and method for implementing a VM APC platform are described. In one embodiment, the VM APC system comprises a process tool for processing a plurality of wafers, a metrology tool for measuring a sample wafer of the plurality of wafers and generating actual metrology data therefor, and a VM model for predicting metrology data for each of the plurality of wafers. The actual metrology data is received from the metrology tool and used to update the VM model. Key variables of the virtual metrology model are updated only in response to a determination that the VM model is inaccurate and parameters of the VM model are updated responsive to receipt of the actual metrology data for the sample wafer of the plurality of wafers. The system also includes an APC controller for receiving the predicted metrology data and the actual metrology data and controlling an operation of the process tool based on the received data.

System and method for implementing a virtual metrology advanced process control platform

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.

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Taipei, Taiwan

Po-Feng Tsai