Static Clustering (Model DIFSCL)



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Static Clustering (Model DIFSCL)

 

This model can handle clustering of boron, phosphorus, arsenic and antimony. Due to the lack of experimental data for phosphorus and antimony the respective clustering is switched off. When the cluster formation process (3.2-10) attains equilibrium, the clustered concentration is related to the active concentration by (3.2-19), and the total concentration amounts to (3.2-20). There, is the equilibrium rate constant which corresponds to the ratio in the case of dynamic clustering.

 

 

In practically all cases there is only one impurity which forms clusters at a given point in the device. We can use this fact to simplify the system of PDEs by approximating in (3.2-19) by the following expression which only involves the charge contribution of the clusters of that impurity (3.2-21).

 

In consistency with the dynamic model an arsenic cluster is formed by three arsenic atoms () and one electron (), and in accordance with the model used in ICECREM [Pic90], a boron cluster consists of six boron atoms () and six electrons () which means that boron clusters are neutral. The total boron and arsenic concentrations amount to (3.2-22) and (3.2-23), respectively.

 

 

We model the active concentration similar to the relation used in ICECREM (3.2-24) for all dopants . To guarantee the correct exponents in (3.2-22) and (3.2-23) we substitute the original values of by artificial values in (3.2-24).These artificial values are for arsenic and for boron. We retain the original values of and for calculating the charge contribution of a clustered dopant (). Then, the net concentration is given by (3.2-25). In (3.2-24) the solid solubility limit is a fit parameter.

 

 

The clustered part of the impurities is immobile () and therefore we get (3.2-26) for the flux with the field term (3.2-27).

 

 

Neumann boundary conditions (zero flux ) are used for all impurities at all boundaries.



next up previous contents
Next: Model Parameters Up: 3.2.2 High Concentration Effects Previous: Dynamic Clustering (Model



Martin Stiftinger
Wed Oct 19 13:03:34 MET 1994