This paper proposes an uncertainty quantification (UQ)-incorporated design optimization technique that integrates UQ considering the statistical characteristics of sensing margin with machine learning (ML) for the design of bitline sense amplifiers (BLSAs) in dynamic random access memory (DRAM), including offset compensation operation. The increased offset due to process variation exacerbated by device scaling degrades the reliability of DRAM read operations, making quantitative analysis of process variation essential. Conventional Monte Carlo (MC)-based UQ analysis provides high accuracy but requires extensive simulation costs. Accordingly, this study systematically defines key input and output variables for conventional BLSA and two representative offset compensation BLSA structures, introduces Sobol sequence-based quasi-Monte Carlo (QMC) sampling to improve simulation efficiency, and generalizes the Standard UQ methodology to predict statistical distributions of circuit performance parameters under process variation. Furthermore, a 3-Stage UQ algorithm that sequentially learns nominal, mean, and standard deviation values is proposed to enhance the efficiency of ML model training. HSPICE simulation results based on the TSMC 65 nm process demonstrated that the proposed 3-Stage UQ method reduced the simulation time required for ML training data generation by an average of 43% compared to the Standard UQ method and achieved excellent prediction performance even in data-limited environments. Additionally, design optimization combined with Bayesian Optimization (BO) confirmed performance improvements across all circuit structures. The methodology presented in this study enables effective process variation-aware optimization for DRAM designs requiring high reliability and yield, and is expected to contribute to next-generation memory circuit design automation.

This work develops a novel, simultaneous multi-VENC and simultaneous multi-slice (SMV+SMS) imaging in a single acquisition for robust phase contrast (PC) MRI. To this end, the pulse sequence was designed to permit concurrent acquisition of multiple VENCs as well as multiple slices on a shared frequency encoding gradient, in which each effective echo time for multiple VENCs was controlled by adjusting net gradient area while multiple slices were simultaneously excited by employing multiband resonance frequency (RF) pulses. For VENC and slice separation, RF phase cycling and gradient blip were applied to create both inter-VENC and inter-slice shifts along phase encoding direction, respectively. With an alternating RF phase cycling that generates oscillating steady-state with low and high signal amplitude, the acquired multi-VENC k-space was reformulated into 3D undersampled k-space by generating a virtual dimension along VENC direction for modulation induced artifact reduction. In vivo studies were conducted to validate the feasibility of the proposed method in comparison with conventional PC MRI. The proposed method shows comparable performance to the conventional method in delineating both low and high flow velocities across cardiac phases with high spatial coverage without apparent artifacts. In the presence of high flow velocity that is above the VENC value, the proposed method exhibits clear depiction of flow signals over conventional method, thereby leading to high VNR image with improved velocity dynamic range.

Dynamic contrast-enhanced magnetic resonance angiography (DCE MRA) has been widely used as a clinical routine for diagnostic assessment of vascular morphology and hemodynamics. It requires high spatial and temporal resolution to capture rapid variation of DCE signals within a limited imaging time. Subtraction-based approaches are typically employed to selectively delineate arteries while eliminating unwanted background signals. Nevertheless, in the presence of subject motion with time, conventional subtraction approaches suffer from incomplete background suppression that impairs the detectability of arteries. In this work, we propose a novel, DCE MRA method that exploits subspace projection (SP) based angiogram separation for robust background suppression. A new, SP-based DCE signal model is introduced, in which images are decomposed into stationary background tissues, motion-induced artifacts, and DCE angiograms of interest. Constrained image reconstruction with sparsity priors is performed to project motion-induced artifacts onto the predefined subspace while extracting DCE angiograms of interest. Simulations and experimental studies validate that the proposed method outperforms existing techniques with increasing reduction factors in suppressing artifacts and noise.