California Institute of Technology

Other

Seiko Epson Corporation

University of Texas System

Gyeong S Hwang

William A Goddard, Iii

Yuzuru Sato

Masamitsu Uehara

Scott A Harrison

Decai Yu

A method for predicting the formation of silicon nanocrystals in an oxide matrix is disclosed. Initially, fundamental data for a set of microscopic processes that can occur during one or more material processing operations are obtained. Kinetic models are then built by utilizing the fundamental data for a set of reactions that can contribute substantially to the formation of silicon nanocrystals in a silicon oxide matrix. Finally, the kinetic models are applied to predict shape, size distribution, spatial arrangements of silicon nanocrystals.

Method for predicting the formation of silicon nanocrystals in embedded oxide matrices

A method for predicting the contribution of silicon interstitials to n-type dopant transient enhanced diffusion during a pn junction formation is disclosed. Initially, fundamental data for a set of microscopic processes that can occur during one or more material processing operations are obtained. The fundamental data are then utilized to build kinetic models for a set of reactions that contribute substantially to an evolution of n-type dopant concentration and electrical activities. The kinetic models are subsequently applied to a simulator to predict temporal and spatial evolutions of concentration and electrical activity profiles of the n-type dopants.

Method for predicting contributions of silicon interstitials to n-type dopant transient enhanced diffusion during a pn junction formation

Techniques for predicting the behavior of dopant and defect components in a substrate lattice formed from a substrate material can be implemented in hardware or software. Fundamental data for a set of microscopic processes that can occur during one or more material processing operations is obtained. Such data can include data representing the kinetics of processes in the set of microscopic processes and the energetics and structure of possible states in the material processing operations. From the fundamental data and a set of external conditions, distributions of dopant and defect components in the substrate lattice are predicted. The distributions of one or more fast components are each predicted by calculating the concentration of the particular fast component for a time period before that fast component reaches its pseudo steady state by solving a first relationship and calculating the concentration of that fast component after the time period by solving a second relationship based on other components, the pseudo steady state of a fast component being a state in which the concentration of that fast component is determined by concentrations of other components. The distribution of BsBi, in addition to the distributions of Bs, BsI, BsI, BsI, BsBi, BsBi, BsBi, BsBiI, BsBiI, BsBi, BsBi, I and I, are calculated by solving the first relationship to predict the distribution of boron after annealing, where Bs and Bi represent substitutional boron and interstitial boron, respectively, and I and Irepresent interstitial silicon and a cluster of n I's, respectively.

Method for predicting the behavior of dopant and defect components

Computer programs and computer-implemented methods for predicting from first principles the behavior of dopants and defects in the processing of electronic materials. The distribution of dopant and defect components in a substrate lattice is predicted based on external conditions and fundamental data for a set of microscopic processes that can occur during material processing operations. The concentration behavior of one or more fast components is calculated in two stages, by solving a first relationship for a time period before the fast component reaches a pseudo steady state at which the concentration of the fast component is determined by concentrations of one or more second components, and by solving a second relationship for a time period after the first component reaches the pseudo steady state. Application of these methods to modeling ultrashallow junction processing is also described.

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Austin, TX, United States of America

Gyeong S Hwang