Naser Khatti Dizabadi

Naser Khatti Dizabadi, Ceyda Elcin Kaya

This paper presents a low-power cache architecture based on the series interconnection of conventional 6-transistor static random-access memory (6T SRAM) cells. The proposed approach aims to reduce leakage power in SRAM-based cache memories without increasing the transistor count of the memory cell itself. In the proposed architecture, adjacent cells within a column are reconfigured in a serial topology, thereby exploiting the stacking effect to suppress leakage current, particularly during hold operation. This architectural modification requires corresponding changes to the addressing and sensing structure of the cache, including adjustments to the column organization and readout path. To evaluate the proposed method, transient simulations were carried out using Keysight ADS. The simulation results show that the proposed architecture reduces leakage power compared with the conventional SRAM interconnection scheme while preserving the use of standard 6T SRAM cells.

Naser Khatti Dizabadi, Peter LoPresti

This paper presents a system-level analytical framework for modeling and minimizing phase noise in body-biased cross-coupled LC-tank voltage-controlled oscillators (LC-VCOs). Building upon Impulse Sensitivity Function (ISF) theory, the impulse sensitivity and noise modulation mechanisms associated with both flicker and thermal noise sources are systematically characterized. By modeling the oscillator as a nonlinear dynamical system and incorporating transistor operation across multiple regions, analytical expressions for device-level noise power spectral densities (PSDs) are derived as functions of transconductance parameters under symmetric body excitation. Using these results, effective ISF representations corresponding to dominant noise sources are formulated, enabling a unified description of noise-to-phase conversion dynamics. The phase noise minimization problem is then cast as an optimization over system parameters, where both DC and RMS components of the effective ISF are analytically evaluated and minimized. This leads to the derivation of three closed-form expressions that explicitly capture the interaction between circuit parameters and the applied body-bias signals. The proposed framework provides insight into parameter sensitivity and design trade-offs in nonlinear oscillator systems and offers generalizable analytical tools for guiding the design of ultra-low phase noise LC-VCOs, as well as for exploring new oscillator architectures.

Naser Khatti Dizabadi

Convolutional neural networks (CNNs) remain a central approach in image classification, but their performance depends strongly on architectural and training choices. This paper presents an empirical ablation-based study of CNN optimization for the CIFAR-10 benchmark. The study evaluates 17 progressive modifications involving training duration, learning-rate scheduling, dropout configuration, pooling strategy, network depth, filter arrangement, and dense-layer design. The goal is to identify which changes improve generalization and which increase complexity without improving performance. The baseline model achieved 79.5\% test accuracy. Extending training duration improved performance steadily, whereas several structural redesigns reduced accuracy despite greater architectural variation. Based on the strongest individual configurations, a weighted ensemble was constructed, achieving 86.38\% accuracy in the reduced-data setting and 89.23\% when trained using the full CIFAR-10 dataset. These results suggest that performance gains in CNN-based classification depend less on indiscriminate increases in depth or parameter count than on careful empirical selection of training and architectural modifications. The study therefore highlights the practical value of ablation-oriented optimization and ensemble learning for small-image classification.

Naser Khatti Dizabadi, Douglas Jussaume

The purpose of this study is to characterize a two-port Bridged-T network using transmission (T) and scattering (S) matrices. Using mathematical derivations, scattering parameters including S11, S12, S21, and S22 have been derived from the T and S matrices to permit a detailed investigation of the network's performance. As two of the most relevant parameters in the design of microstrip filters, both the magnitude and phase of S11 and S21 have been parametrically calculated after normalizing the frequency. Furthermore, when the inductors L1 and L2 are identical, all even coefficients of the numerator polynomial in the S11 transfer function are eliminated, leaving only the odd coefficients behind. Based on this feature, the bridged-T circuit is designed to operate as a high-pass filter. Therefore, the magnitude and phase of both S11 and S21 have been simulated for the designed filter with a corner frequency of 1 GHz. Simulation results performed by Keysight ADS show that S11 and S21 for the high-pass filter built upon the bridged-T network have sharp roll-off ratios of -30dB/GHz and -32dB/GHz respectively.