We present a simulation system which meets the requirements for practical application of inverse modeling in a professional environment. A tool interface for the integration of arbitrary simulation tools at the user level is introduced and a methodology for the formation of simulation networks is described. A Levenberg-Marquardt optimizer automates the inverse modeling procedure. Strategies for the efficient execution of simulation tools are discussed. An example demonstrates the extraction of doping profile information on the basis of electrical measurements.

Though causal order of message delivery simplifies the design and development of distributed applications, the overhead of enforcing it is not negligible. We claim that a causal order algorithm which does not send any redundant information is efficient in the sense of communication overhead. We characterize and classify the redundant information into four categories: information regarding just delivered, already delivered, just replaced, and already replaced messages. We propose an efficient causal multicast algorithm which prevents propagation of these redundant information. Our algorithm sends less amount of control information needed to ensure causal order than other existing algorithms and can also be applied to systems whose communication channels are not FIFO. Since our algorithm's communication overhead increases relatively slowly as the number of processes increases, it shows good scalability feature. The potential of our algorithm is shown by simulation study.