This paper extends the model in (Wajih Dalai et al, Proc. of Int. Test Conf., p.518-523, 1999) to be more realistic by including the effects of the test fixtures between a device under test and a tester. The paper enables analyzing the trade-offs that arise between the predicted yield and the required overall test environment timing accuracy (OTETA) which involves the tester overall timing accuracy (OTA) and the test fixture impact. We specifically focus on the application of the extended model to predict the test yield of standard high-speed interconnects, such as PCI Express, RapidIO, and HyperTransport. The extended model reveals that achieving an actual yield of 80% with a defect level of 300 DPM (defects per million) requires an equivalent OTETA that is about half the acceptable absolute limit of the tested parameter.

CMAC neural network is one useful learning technique based on the cerebellum's motor behavior. CMAC uses a table look-up method to resolve the complex non-linear system instead of a numerical calculation method. The result is in better and faster controller for nonlinear dynamical system. In this paper, we propose a CMAC neural network for controlling a non-linear system. The simulation results show that the proposed CMAC controllers for a simple non-linear function and a DC motor speed control reduce tracking errors and improve the stability of its learning controllers. The validity of the proposed CMAC controller is also proved by the real-time tension control. Besides, hardware design of CMAC for control application has been implemented to confirm the proposed approach and architecture.

For the original article see ibid., vol.8, no.3, p.257-67 (1989). The commenters point out that their parallel algorithm provides the maximum planar subgraph, and they compare it to the maximal planar subgraph provided by R. Jayakumar et al. in the above-mentioned paper. The space-time product complexity is also compared.>

A statistical learning model called the multinomial conjunctoid is reviewed. Multinomial conjunctoids are based on a well-developed, statistical-decision-theory framework, which guarantees that conjunctoid learning will converge to optimal states over learning trials and the learning will be fast during these trials. In addition, a prototype multinomial conjunctoid module based on CMOS VLSI technology is introduced.>