In the realm of Design for Manufacturability (DFM) optimization, Pattern-Based Layout Optimization (PBLO) has been a go-to approach for detecting and repairing DFM violations. However, to enhance the effectiveness of DFM rules in addressing hotspots, it becomes imperative to encompass a broader array of design situations (layout contexts). This expansion leads to an increased number of potential fixing guidance “hints”. Nonetheless, employing a static fixing hint order, unaware to the specific in-design topologies, can potentially diminish the output metrics i.e., fixing rate and runtime performance. In pursuit of optimizing these output metrics, we present an ML-powered PBLO workflow. In this innovative approach, a Machine Learning (ML) model is trained using an extensive dataset of preranked fixing guidance hints that are associated with a DFM rule. The topology aware supervised ML model is trained to dynamically guide and select the most suitable in-design fixing guidance order per situation, ultimately leading to an improved fixing rate, runtime and quality of results. In this study, we illustrate a workflow and mechanism for seamlessly integrating machine learning capabilities into the in-design fixing router. This involves developing multiclass machine learning algorithms and models to facilitate the generation of an optimal fixing guidance sequence.
Stress technologies such as stress liners are used to improve the performance of advanced CMOS devices. Due to the contextual situation of a transistor in the physical design layout, unintended stress from neighboring cells can cause variations in the transistor characteristics. This effect is called Layout Dependent Effect (LDE). In this work we propose a fast method to detect outlier transistors due to the LDEs by profiling and sampling them from the VLSI design with millions of transistors and many devices. The proposed method can reduce the TAT for quantitative evaluation of the LDE for design layouts that have not passed the LVS. We also propose a pattern matching based method to search motifs created by encapsulating neighborhood of outlier transistors with large Vth variations. This enables designers to trace such LDE hotspot patterns and thereby outlier transistors during the design phase.
Systematic defects have drawn a lot of focus from the semiconductor industry, especially in the technology development and early technology ramp. However, random defects are still dominant when the technology is mature and in highvolume manufacturing. Historically, foundries have run critical area analysis on incoming designs in order to identify the yield-limiting failure modes and estimate the yield loss. However, with growing design complexity in advanced technology nodes, the calculation runtime of critical area has increased from hours to days and even week(s). Also FINFET brings their own challenges and new failure modes such as transistor-related defectivity and inter-layer interactions. Meanwhile, it has become more and more challenging to obtain accurate defect density by failure mode. In this paper, GlobalFoundries and Cadence describe the motivations that drove their partnership to develop a new generation of critical area analysis with adaptive sampling to reduce runtime while maintaining accuracy, especially while taking into account connectivity and transistor defectivity. After reviewing the principle and challenges of critical area calculation and yield estimation, two new methodologies of yield modeling using critical area analysis are given to address these challenges. The first methodology avoids the costly and complicated process of defect density calibration. The second methodology fulfills the wafer-based yield projection with critical area normalization and machine learning.
Process and reliability risks have become critically important during mass production at advanced technology nodes even with Extreme Ultraviolet Lithography (EUV) illumination. In this work, we propose a design-for-manufacturability solution using a set of new rules to detect high risk design layout patterns. The proposed methods improve design margins while avoiding area overhead and complex design restrictions. In addition, the proposed method introduces an in-design pattern replacement with automatically generated fixing hints to improve all matched locations with identified patterns.
We identify most recent sources of transistor layout dependent effects (LDE) such as stress, lithography,
and well proximity effects (WPE), and outline modeling and analysis methods for 28 nm. These methods
apply to custom layout, standard cell designs, and context-aware post-route analysis. We show how IC
design teams can use a model-based approach to quantify and analyze variability induced by LDE. We
reduce the need for guard-bands that negate the performance advantages that stress brings to advanced
process technologies.
The impact of lithography-induced systematic variations on the parametric behavior of cells and chips designed on a TI
65nm process has been studied using software tools for silicon contour prediction, and design analysis from contours.
Using model-based litho and etch simulation at different process conditions, contours were generated for the poly and
active layers of standard cells in multiple contexts. Next, the extracted transistor-level SPICE netlists (with annotated
changes in CD) were simulated for cell delay and leakage. The silicon contours predicted by the model-based litho tools
were validated by comparing CDs of the simulated contours with SEM images. A comparative analysis of standard cells
with relaxed design rules and restricted pitch design rules showed that restrictive design rules help reduce the variation
from instance to instance of a given cell by as much as 15%, but at the expense of an area penalty. A full-chip variability
analysis flow, including model-based lithography and etch simulation, captures the systematic variability effects on
timing-critical paths and cells and allows for comparison of the variability of different cells and paths in the context of a
real design.
With increasing chip sizes and shrinking device dimensions, on-chip semiconductor process variation can no longer be
ignored in the design and signoff static timing analysis of integrated circuits. An important parameter affecting CMOS
technologies is the gate length (Lgate) of a transistor. In modern technologies, significant spatial intra-chip variability of
transistor gate lengths, which is systematic as opposed to random, can lead to relatively large variations in circuit path
delays. Spatial variations in Lgate affect circuit timing properties, which can lead to timing errors and performance loss.
To maximize performance and process utilization in microprocessor designs, we have developed and validated a timing
analysis methodology based on accurate silicon contour prediction from drawn layout and contour-based extraction of
our designs. This allows for signoff timing without unnecessarily large margins, thereby reducing chip area and
maximizing performance while ensuring chip functionality, improved process utilization and yield. In this paper, we
describe the chip timing methodology, its validation and implementation in microprocessor design.
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