Several new memories are being studied as candidates of future DRAM that seems difficult to be scaled. However, the read signal in these new memories needs to be amplified in a single-end manner with reference signal supplied if they are aimed for being applied to the high-density main memory. This scheme, which is fortunately not necessary in DRAM's 1/2Vdd pre-charge sense amp, can become a serious bottleneck in the new memory development, because the device electrical parameters in these new memory cells are prone to large cell-to-cell variations without exception. Furthermore, the extent to which the parameter fluctuates in data “1” is generally not the same as in data “0”. In these situations, a new sensing scheme is proposed that can minimize the sensing error rate for high-density single-end emerging memories like STT-MRAM, ReRAM and PCRAM. The scheme is based on averaging multiple dummy cell pairs that are written “1” and “0” in a weighted manner according to the fluctuation unbalance between “1” and “0”. A detailed analysis shows that this scheme is effective in designing 128Mb 1T1MTJ STT-MRAM with the results that the required TMR ratio of an MTJ can be relaxed from 130% to 90% for the fluctuation of 6% sigma-to-average ratio of MTJ resistance in a 16 pair-dummy cell averaging case by using this technology when compared with the arithmetic averaging method.

This paper reviews two simple numerical algorithms particularly useful in Computational ElectroMagnetics (CEM): the Weighted Averages (WA) algorithm and the Double Exponential (DE) quadrature. After a short historical introduction and an elementary description of the mathematical procedures underlying both techniques, they are applied to the evaluation of Sommerfeld integrals, where WA and DE combine together to provide a numerical tool of unprecedented quality. It is also shown that both algorithms have a much wider range of applications. A generalization of the WA algorithm, able to cope with integrands including products of Bessel and similar oscillatory functions, is described. Similarly, the original DE algorithm is adapted with exceptional results to the evaluation of the multidimensional singular integrals arising in the discretization of Integral-Equation based CEM formulations. The new possibilities of WA and DE algorithms are demonstrated through several practical numerical examples.

An effective learning method for the fuzzy ARTMAP in the recognition of noisy input patterns is presented. the weight vectors of the system are updated using the weighted average of the noisy input vector and the weight vector itself. This method leads to stable learning and prevents the excessive update of the weight vectors which may cause performance degradation. Simulation results show that the proposed method not only reduces the generation of spurious categories, but aloso increases the recognition ratio in the noisy environment.