In modern DRAM design, IR-drop has emerged as a critical factor leading to operation failures, primarily due to reduced metal resources and increased supply current demands. Consequently, estimating and addressing IR-drop throughout the DRAM design process has become a crucial sign-off consideration. The proposed comprehensive methodology for the power delivery network (PDN) includes accurate and efficient estimation at two key stages: early in the design process (Early PDN) and during post-physical design sign-off validation (Iterative PDN). The Early PDN approach employs simplified modeling to achieve reasonable accuracy, serving as a guideline for the initial PDN design. Subsequently, the Iterative PDN method provides a fast yet detailed analysis of the entire DRAM, including the PDN, ensuring it is suitable for final sign-off. By applying this methodology, PDN behavior can be effectively predicted and optimized, ensuring robustness. This enhances the reliability and performance of DRAM designs.

Analog/mixed-signal circuit design encounters significant challenges due to performance degradation from process, voltage, and temperature (PVT) variations. To achieve commercial-grade reliability, iterative manual design revisions and extensive statistical simulations are required. While several studies have aimed to automate variation-aware analog design to reduce time-to-market, the substantial mismatches in real-world wafers have not been thoroughly addressed. In this paper, we present GLOVA, an analog circuit sizing framework that effectively manages the impact of diverse random mismatches to improve robustness against PVT variations. In the proposed approach, risk-sensitive reinforcement learning is leveraged to account for the reliability bound affected by PVT variations, and ensemble-based critic is introduced to achieve sample-efficient learning. For design verification, we also propose μ-σ evaluation and simulation reordering method to reduce simulation costs of identifying failed designs. GLOVA supports verification through industrial-level PVT variation evaluation methods, including corner simulation as well as global and local Monte Carlo (MC) simulations. Compared to previous state-of-the-art variation-aware analog sizing frameworks, GLOVA achieves up to $80.5 \times$ improvement in sample efficiency and $76.0 \times$ reduction in time.