Data-driven solutions of ill-posed inverse problems arising from doping reconstruction in semiconductors

The non-destructive estimation of doping concentrations in semi- conductor devices is of paramount importance for many applications ranging from crystal growth to defect and inhomogeneity detec- tion. A number of technologies (such as LBIC, EBIC and LPS) have been developed which allow the detection of doping variations via photovoltaic effects. The idea is to illuminate the sample at several positions and detect the resulting voltage drop or current at the con- tacts. We model a general class of such photovoltaic technologies by ill-posed global and local inverse problems based on a drift-diffusion system which describes charge transport in a self-consistent elec- trical field. The doping profile is included as a parametric field. To numerically solve a physically relevant local inverse problem, we present three approaches, based on least squares, multilayer percep- trons, and residual neural networks. Our data-driven methods recon- struct the doping profile for a given spatially varying voltage signal induced by a laser scan along the sample’s surface. The methods are trained on synthetic data sets which are generated by finite vol- ume solutions of the forward problem. While the linear least square method yields an average absolute error around 10%, the nonlinear networks roughly halve this error to 5%.