The effect of the heterojunction interface on the performance of high bandgap InxGa1−xP:Te/Al0.6Ga0.4As:C tunnel junctions (TJs) was investigated. The insertion of 30 Å of GaAs:Te at the junction interface resulted in a peak current of 1000 A/cm2 and a voltage drop of ∼3 mV for 30 A/cm2 (2000× concentration). The presence of this GaAs interfacial layer also improved the uniformity across the wafer. Modeling results are consistent with experimental data and were used to explain the observed enhancement in TJ performance. This architecture could be used within multijunction solar cells to extend the range of usable solar concentration with minimal voltage drop.

The performance of n+-InGaP(Te)/p+-AlGaAs(C) high band gap tunnel junctions (TJ) is critical for achieving high efficiency in multijunction photovoltaics. Several limitations for as grown and annealed TJ can be attributed to the Te doping of InGaP and its behavior at the junction interface. Te atoms in InGaP tend to get attached at step edges, resulting in a Te memory effect. In this work, we use the peak tunneling current (Jpk) in this TJ as a diagnostic tool to study the behavior of the Te dopant at the TJ interface. Additionally, we used our understanding of Te behavior at the interface, guided by device modeling, to modify the Te source shut-off procedure and the growth rate. These modifications lead to a record performance for both the as-grown (2000 A/cm2) and annealed (1000 A/cm2) high band gap tunnel junction.

An adaptive steady-state optimization algorithm is presented and applied to the problem of optimizing the production of biomass in continuous fermentation processes. The algorithm requires no modeling information but is based on an on-line identified linear model, locates the optimum dilution rate, and maintains the chemostat at its optimum operating condition at all times. The behavior of the algorithm is tested against a dynamic model of a chemostat that incorporates metabolic time delay, and it is shown that large disturbances in the subtrate feed concentration and the specific growth rate, causing a shift in the optimum, are handled well. The developed algorithm is also used to drive a methylotroph single-cell production process to its optimum.

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Process control of anaerobic digesters is a particularly challenging problem because of the diversity of possible causes that can lead to digester imbalance. Conventional control schemes can fail in consequence of a reversal in the sign of the steady-state gain caused by some type of disturbance. In this work we present an expert system approach that takes into account the particularity of this process. The developed algorithm is demonstrated to compensate successfully for changes in the digester feed medium when simulated against a model for a continuous anaerobic digester.