Digital clock synchronization is critical for ensuring accurate timing across distributed systems, especially in high-performance computing and communication networks. Traditional synchronization methods often face challenges related to drift, jitter, and latency, which can lead to timing inaccuracies. This study introduces a novel Cyclic Rotation Algorithm (CRA) designed to enhance the precision of digital clock synchronization. The CRA method involves a systematic rotation of time stamps in a cyclic manner, effectively minimizing timing discrepancies and aligning clocks with greater accuracy.

The algorithm's performance was evaluated through extensive simulations and real-world testing across various network environments. Results demonstrate a significant improvement in synchronization accuracy, with the CRA consistently outperforming existing synchronization techniques in reducing clock drift and maintaining stability over extended periods.

Additionally, the CRA offers scalability and robustness, making it suitable for integration into diverse digital systems. This research contributes to the advancement of clock synchronization techniques, providing a reliable solution for applications where precision timing is paramount.

Electromagnetic Compatibility (EMC) testing is a critical step in the development and certification of electronic devices to ensure they function correctly in their intended electromagnetic environment without causing or being susceptible to unacceptable electromagnetic interference [1]. EMC tests often involve applying specific electromagnetic test signals and monitoring the device's response or measuring its emissions. These test signals, particularly those used for immunity testing (e.g., transient pulses, modulated sine waves), are inherently time-series data with complex temporal characteristics. Accurately predicting the behavior or required parameters of these test signals under various conditions or extrapolating limited measurements could significantly optimize testing procedures, reduce test time, and improve the efficiency of EMC compliance efforts [27]. Traditional signal processing techniques [7, 11, 12, 13, 14] may struggle with the non-linear and potentially non-stationary nature of some EMC phenomena and test signals [5, 6]. Deep learning, specifically Long Short-Term Memory (LSTM) networks, has demonstrated exceptional capabilities in modeling and predicting complex sequential data [15, 16, 17, 18, 19]. This article proposes and outlines a methodology for predicting EMC test signal characteristics using LSTM networks. We discuss the conceptual framework for data acquisition, model architecture design, training, and evaluation, drawing upon principles from time-series analysis [5, 6], neural networks [2, 4, 10, 15, 16, 17, 18, 19], and signal processing [7, 11, 12, 13, 14]. The potential benefits include enhanced test efficiency, improved understanding of signal behavior, and the possibility of generating synthetic test data for simulation purposes [24].

With the architecture complexity of silicon in high-performance computing (HPC) and graphics processing units (GPUs) growing, reliability, scalability, and first-time-right silicon cannot be achieved without the introduction of advanced Design for Test (DFT) methodologies. This paper addresses the peculiarities of DFT magnetization to cope with the characteristics of HPC and GPU environment issues: massive parallelism, depth pipelining, multi-clock, power domains, and rising thermal and power density. It covers basic techniques, including scan-based testing, built-in self-test (BIST), logic BIST (LBIST), and a modular and hierarchical test planning framework. Additionally, the paper studies the related key infrastructural pieces, such as test access mechanisms (IJTAG, IEEE 1500), remote debug orchestration, and centralized test control units. Additionally, emerging trends like AI/ML-enabled ATPG, in-field telemetry, predictive maintenance, and DFT innovations in the contexts of chipset-based and 3D-integrated architecture alter the test requirements for the overall multi-die system. It provides best practices in early DFT planning, modular IP reuse, scan chain optimization, and power-aware test pattern generation to obtain high test coverage while maintaining silicon performance. This work presents actionable insights for high-yield silicon design and validation in the next-generation compute platform landscape. It is aimed at silicon architects, DFT engineers, and verification professionals.