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A 7.5 Gb/s/pin 8-Gb LPDDR5 SDRAM is implemented in a 1 $\times$ nm DRAM process. Various techniques are applied to achieve higher bandwidth and lower power than LPDDR4X. To increase data rate, a WCK clocking scheme that is less vulnerable to power noise is adopted and a non-target ODT mode is proposed to reduce reflection noise in a two-rank system. A couple of techniques are proposed for saving power. To reduce self-refresh power, this chip supports deep sleep mode (DSM). In DSM, the leakage current of internal voltages decreases by disabling internal voltage generators that are not related with a self-refresh operation. Dynamic voltage frequency scaling (DVFS) is adopted to reduce read and write operation power and when writing all zeros data, an internal data copy function can be used for reducing write operation power. Last, a ZQ calibration scheme that shares one ZQ resistor (RZQ) and automatically executes ZQ calibration is presented. The proposed LPDDR5 DRAM operates up to 7.5 Gb/s on an automatic test equipment (ATE) and 6.4 Gb/s on a prototype system. Read and write power decrease by 21% and 33% compared to LPDDR4X at 4.266 Gb/s, and self-refresh power is reduced by 25% in DSM. [ABSTRACT FROM AUTHOR]

Abstract: Finite element model updating is an optimization problem to identify and correct uncertain modeling parameters. In conventional model updating, physically incompatible criteria, which designate differences between analytical and experimental results, are combined into a single-objective function using weighting factors. There are no general rules for selecting the weighting factors since they are not directly related to the dynamic behavior of the updated model. Thus, a necessary approach is to solve the time-consuming optimization problem repeatedly by varying the values of weighting factors until a satisfactory solution is obtained. In this work, an interactive multiobjective optimization technique called satisficing trade-off method is introduced to avoid the difficulty. It is relatively easy to state what kind of solutions is satisfactory considering the correlations of the initial FE model with the experimental results, the importance of individual modal properties, and measurement errors, etc. The satisficing trade-off method uses this information directly in the optimization process and finds a Pareto solution which is nearest to the given information. Moreover, as the method provides the tangent hyperplane which approximates the Pareto surface in the neighborhood of the obtained Pareto solution, the desired updated model can be found in a few iterations. [Copyright &y& Elsevier]