The Wrong Kind of Lean: Over-Commitment and Under-Represented Skills on Technology Teams

This paper reports on results from five companies in the aerospace and automotive industries to show that over-commitment of technical professionals and under-representation of key skills on technology development and transition teams seriously impairs team performance. The research finds that 40 percent of the projects studied were inadequately staffed, resulting in weaker team communications and alignment. Most importantly, the weak staffing on these teams is found to be associated with a doubling of project failure rate to reach full production. Those weakly staffed teams that did successfully insert technology into production systems were also much more likely than other teams to have development delays and late engineering changes. The conclusion suggests that the expense of project failure, delay and late engineering changes in these companies must greatly out-weigh the savings gained from reduced staffing costs, and that this problem is likely going to be found in other technology-intensive firms intent on seeing project budgets as a cost to be minimized rather than an investment to be maximized.

Nanocrystalline Ge Flash Memories: Electrical Characterization and Trap Engineering

Conventional floating gate non-volatile memories (NVMs) present critical issues for device scalability beyond the sub-90 nm node, such as gate length and tunnel oxide thickness reduction. Nanocrystalline germanium (nc-Ge) quantum dot flash memories are fully CMOS compatible technology based on discrete isolated charge storage nodules which have the potential of pushing further the scalability of conventional NVMs. Quantum dot memories offer lower operating voltages as compared to conventional floating-gate (FG) Flash memories due to thinner tunnel dielectrics which allow higher tunneling probabilities. The isolated charge nodules suppress charge loss through lateral paths, thereby achieving a superior charge retention time. Despite the considerable amount of efforts devoted to the study of nanocrystal Flash memories, the charge storage mechanism remains obscure. Interfacial defects of the nanocrystals seem to play a role in charge storage in recent studies, although storage in the nanocrystal conduction band by quantum confinement has been reported earlier. In this work, a single transistor memory structure with threshold voltage shift, Vth, exceeding ~1.5 V corresponding to interface charge trapping in nc-Ge, operating at 0.96 MV/cm, is presented. The trapping effect is eliminated when nc-Ge is synthesized in forming gas thus excluding the possibility of quantum confinement and Coulomb blockade effects. Through discharging kinetics, the model of deep level trap charge storage is confirmed. The trap energy level is dependent on the matrix which confines the nc-Ge.

Generalizing on Multiple Grounds: Performance Learning in Model-Based Technology

This thesis explores ways to augment a model-based diagnostic program with a learning component, so that it speeds up as it solves problems. Several learning components are proposed, each exploiting a different kind of similarity between diagnostic examples. Through analysis and experiments, we explore the effect each learning component has on the performance of a model-based diagnostic program. We also analyze more abstractly the performance effects of Explanation-Based Generalization, a technology that is used in several of the proposed learning components.