Abstract:
The present disclosure describes a method of optimizing a design for manufacture (DFM) simulation. The method includes receiving an integrated circuit (IC) design data having a feature, receiving a process data having a parameter or a plurality of parameters, performing the DFM simulation, and optimizing the DFM simulation. The performing the DFM simulation includes generating a simulation output data using the IC design data and the process data. The optimizing the DFM simulation includes generating a performance index of the parameter or the plurality of parameters by the DFM simulation. The optimizing the DFM simulation includes adjusting the parameter or the plurality of parameters at outer loop, middle loop, and the inner loop. The optimizing the DFM simulation also includes locating a nadir of the performance index of the parameter or the plurality of parameters over a range of the parameter or the plurality of parameters.
Abstract:
The present disclosure provides a method. The method includes gathering advanced process control (APC) data from a subset of available wafers and a subset of available processing chambers. The method includes establishing a matrix that contains a plurality of cells. The cells each correspond to one of the available wafers and one of the available processing chambers. The matrix is partially filled by populating cells for which the APC data has been gathered. The method includes determining a plurality of chamber-coverage-rate (CCR) parameters associated with the matrix. The method includes optimizing the CCR parameters through an iteration process to obtain optimized CCR parameters. The method includes predicting an APC data value for a designated cell of the matrix based on the optimized CCR parameters. The designated cell is an empty cell before the predicting and is populated by the predicting.
Abstract:
The present disclosure provides a method of fabricating a semiconductor device. The method includes collecting a plurality of manufacturing data sets from a plurality of semiconductor processes, respectively. The method includes normalizing each of the manufacturing data sets in a manner so that statistical differences among the manufacturing data sets are reduced. The method includes establishing a database that includes the normalized manufacturing data sets. The method includes normalizing the database in a manner so that the manufacturing data sets in the normalized database are statistically compatible with a selected one of the manufacturing data sets. The method includes predicting performance of a selected one of the semiconductor processes by using the normalized database. The selected semiconductor process corresponds to the selected manufacturing data set. The method includes controlling a semiconductor processing machine in response to the predicted performance.
Abstract:
The present disclosure provides a semiconductor manufacturing method. The method includes performing a first process to a first plurality of semiconductor wafers; determining a sampling rate to the first plurality of semiconductor wafers based on process quality; determining sampling fields and sampling points to the first plurality of semiconductor wafers; measuring a subset of the first plurality of semiconductor wafers according to the sampling rate, the sampling fields and the sampling points; modifying a second process according to the measuring; and applying the second process to a second plurality of semiconductor wafers.
Abstract:
A method comprises computing respective regression models for each of a plurality of failure bins based on a plurality of failures identified during wafer electrical tests. Each regression model outputs a wafer yield measure as a function of a plurality of device performance variables. For each failure bin, sensitivity of the wafer yield measure to each of the plurality of device performance variables is determined, and the device performance variables are ranked with respect to sensitivity of the wafer yield measure. A subset of the device performance variables which have highest rankings and which have less than a threshold correlation with each other are selected. The wafer yield measures for each failure bin corresponding to one of the selected subset of device performance variables are combined, to provide a combined wafer yield measure. At least one new process parameter value is selected to effect a change in the one device performance variable, based on the combined wafer yield measure. The at least one new process parameter value is to be used to process at least one additional wafer.
Abstract:
The present disclosure provides a semiconductor manufacturing method. The method includes performing a first process to a wafer; measuring the wafer for wafer data after the first process; securing the wafer on an E-chuck in a processing chamber; collecting sensor data from a sensor embedded in the E-chuck; adjusting clamping forces to the E-chuck based on the wafer data and the sensor data; and thereafter performing a second process to the wafer secured on the E-chuck in the processing chamber.
Abstract:
An apparatus includes a process chamber configured to perform an ion implantation process. A cooling platen or electrostatic chuck is provided within the process chamber. The cooling platen or electrostatic chuck is configured to support a semiconductor wafer. The cooling platen or electrostatic chuck has a plurality of temperature zones. Each temperature zone includes at least one fluid conduit within or adjacent to the cooling platen or electrostatic chuck. At least two coolant sources are provided, each fluidly coupled to a respective one of the fluid conduits and configured to supply a respectively different coolant to a respective one of the plurality of temperature zones during the ion implantation process. The coolant sources include respectively different chilling or refrigeration units.