Taiwan Semiconductor Manufacturing Comp. Ltd.

Elite Semiconductor Memory Technology, Inc

Jo Fei Wang

Jong-I Mou

Sunny Wu

Po-Feng Tsai

Chin-Hsiang Lin

Yen-Di Tsen

Chia-Tong Ho

Chun-Hsien Lin

Andy Tsen

Keung Hui

Ming-Yu Fan

Chih-Wei Hsu

Chih-Tien Chang

Tze-Liang Lee

Chi-Ming Yang

Chun-Lin Louis Chang

Chyi Shyuan Chern

Chung Zen Chen

Hsin-Hsien Wu

Yu-Jen Cheng

Jun-Lin Yeh

Shin-Rung Lu

Jih-Jse Lin

Zin-Chang Wei

Jin-Ning Sung

Wen-Pin Liu

Mei-Jen Wu

Chih-Sheng Shih

Jill Wang

Hsin Kuan

Jung-Chang Chen

Shun-Ping Wang

Monghsung Chuang

Fu-Min Huang

Kun-Ming Chen

Dung-Yian Hsieh

Hui-Ru Lin

I-Ching Chu

An apparatus and a method for controlling critical dimension (CD) of a circuit is provided. An apparatus includes a controller for receiving CD measurements at respective locations in a circuit pattern in an etched film on a first substrate and a single wafer chamber for forming a second film of the film material on a second substrate. The single wafer chamber is responsive to a signal from the controller to locally adjust a thickness of the second film based on the measured CD's. A method provides for etching a circuit pattern of a film on a first substrate, measuring CD's of the circuit pattern, adjusting a single wafer chamber to form a second film on a second semiconductor substrate based on the measured CD. The second film thickness is locally adjusted based on the measured CD's.

Multi-zone temperature control for semiconductor wafer

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

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

An apparatus, a system and a method are disclosed. An exemplary apparatus includes a wafer processing chamber. The apparatus further includes radiant heating elements disposed in different zones and operable to heat different portions of a wafer located within the wafer processing chamber. The apparatus further includes sensors disposed outside the wafer processing chamber and operable to monitor energy from the radiant heating elements disposed in the different zones. The apparatus further includes a computer configured to utilize the sensors to characterize the radiant heating elements disposed in the different zones and to provide a calibration for the radiant heating elements disposed in the different zones such that a substantially uniform temperature profile is maintained across a surface of the wafer.

Real-time calibration for wafer processing chamber lamp modules

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 system and method of automatically calculating boundaries for a semiconductor fabrication process. The method includes selecting a first parameter for monitoring during a semiconductor fabrication process. A first set of values for the first parameter are received and a group value of the first set is determined. Each value in the first set of values is normalized. A first weighting factor is selected based on a number of values in the first set. The embodiment also includes generating a first and a second boundary value as a function of the weighting factor, the first set normalized values and the group value of the first set and applying the first and second boundary values to control the semiconductor fabrication process.

Systems and methods of automatic boundary control for semiconductor processes

An embodiment is a method for semiconductor processing control. The method comprises identifying a key process stage from a plurality of process stages based on a parameter of processed wafers, forecasting a trend for a wafer processed by the key process stage and some of the plurality of process stages based on the parameter, and dispatching the wafer to one of a first plurality of tools in a tuning process stage. The one of the first plurality of tools is determined based on the trend.

Semiconductor processing dispatch control

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

The present disclosure describes a semiconductor manufacturing apparatus. The apparatus includes a processing chamber designed to perform a process to a wafer; an electrostatic chuck (E-chuck) configured in the processing chamber and designed to secure the wafer, wherein the E-chuck includes an electrode and a dielectric feature formed on the electrode; a tuning structure designed to hold the E-chuck to the processing chamber by clamping forces, wherein the tuning structure is operable to dynamically adjust the clamping forces; a sensor integrated with the E-chuck and sensitive to the clamping forces; and a process control module for controlling the tuning structure to adjust the clamping forces based on pre-measurement data from the wafer and sensor data from the sensor.

E-chuck with automated clamped force adjustment and calibration

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

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Hsin-Chu, Taiwan

Jo Fei Wang