Ever increasing need for tighter on-product overlay (OPO), as well as enhanced accuracy in overlay metrology and methodology, is driving semiconductor industry’s technologists to innovate new approaches to OPO measurements. In case of High Volume Manufacturing (HVM) fabs, it is often critical to strive for both accuracy and robustness. Robustness, in particular, can be challenging in metrology since overlay targets can be impacted by proximity of other structures next to the overlay target (asymmetric effects), as well as symmetric stack changes such as photoresist height variations. Both symmetric and asymmetric contributors have impact on robustness. Furthermore, tweaking or optimizing wafer processing parameters for maximum yield may have an adverse effect on physical target integrity. As a result, measuring and monitoring physical changes or process abnormalities/artefacts in terms of new Key Performance Indicators (KPIs) is crucial for the end goal of minimizing true in-die overlay of the integrated circuits (ICs). IC manufacturing fabs often relied on CD-SEM in the past to capture true in-die overlay. Due to destructive and intrusive nature of CD-SEMs on certain materials, it’s desirable to characterize asymmetry effects for overlay targets via inline KPIs utilizing YieldStar (YS) metrology tools. These KPIs can also be integrated as part of (μDBO) target evaluation and selection for final recipe flow. In this publication, the Holistic Metrology Qualification (HMQ) flow was extended to account for process induced (asymmetric) effects such as Grating Imbalance (GI) and Bottom Grating Asymmetry (BGA). Local GI typically contributes to the intrafield OPO whereas BGA typically impacts the interfield OPO, predominantly at the wafer edge. Stack height variations highly impact overlay metrology accuracy, in particular in case of multi-layer LithoEtch Litho-Etch (LELE) overlay control scheme. Introducing a GI impact on overlay (in nm) KPI check quantifies the grating imbalance impact on overlay, whereas optimizing for accuracy using self-reference captures the bottom grating asymmetry effect. Measuring BGA after each process step before exposure of the top grating helps to identify which specific step introduces the asymmetry in the bottom grating. By evaluating this set of KPI's to a BEOL LELE overlay scheme, we can enhance robustness of recipe selection and target selection. Furthermore, these KPIs can be utilized to highlight process and equipment abnormalities. In this work, we also quantified OPO results with a self-contained methodology called Triangle Method. This method can be utilized for LELE layers with a common target and reference. This allows validating general μDBO accuracy, hence reducing the need for CD-SEM verification.