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Lithographical hotspot (LH) detection using deep learning (DL) has received much attention in the recent years. It happens mainly due to the facts the DL approach leads to a better accuracy over the traditional, state-of-the-art programming approaches. The purpose of ths study is to compare existing data augmentation (DA) techniques for the integrated circuit (IC) mask data using DL methods. DA is a method which refers to the process of creating new samples similar to the training set, thereby helping to reduce the gap between classes as well as improving the performance of the DL system. Experimental results suggest that the DA methods increase overall DL models performance for the hotspot detection tasks.
The hotspot detection has received much attention in the recent years due to a substantial mismatch between lithography wavelength and semiconductor technology feature size. This mismatch causes diffraction when transferring the layout from design onto a silicon wafer. As a result, open or short circuits (i.e. lithography hotspots) are more likely to be produced. Additionally, increasing numbers of semiconductors devices on a wafer required more time for the lithography hotspot detection analysis. In this work, we propose a fast and accurate solution based on novel artificial neural network (ANN) architecture for precise lithography hotspot detection using a convolution neural network (CNN) adopting a state of-the-art technique. The experimental results showed that the proposed model gained accuracy improvement over current state-of-theart approaches. The final code has been made publicly available.
Due to the lack of sophisticated component libraries for microelectromechanical systems (MEMS), highly optimized MEMS sensors are currently designed using a polygon driven design flow. The advantage of this design flow is its accurate mechanical simulation, but it lacks a method for analyzing the dynamic parasitic electrostatic effects arising from the electric coupling between (stationary) wiring and structures in motion. In order to close this gap, we present a method that enables the parasitics arising from in-plane, sensor-structure motion to be extracted quasi-dynamically. With the method's structural-recognition feature we can analyze and optimize dynamic parasitic electrostatic effects.
Electromigration (EM) is becoming a progressively severe reliability challenge due to increased interconnect current densities. A shift from traditional (post-layout) EM verification to robust (pro-active) EM aware design - where the circuit layout is designed with individual EM-robust solutions - is urgently needed. This tutorial will give an overview of EM and its effects on the reliability of present and future integrated circuits (ICs). We introduce the physical EM process and present its specific characteristics that can be affected during physical design. Examples of EM countermeasures which are applied in today’s commercial design flows are presented. We show how to improve the EM-robustness of metallization patterns and we also consider mission proiles to obtain application-oriented current density limits. The increasing interaction of EM with thermal migration is investigated as well. We conclude with a discussion of application examples to shift from the current post layout EM verification towards an EM aware physical design process. Its methodologies, such as EM-aware routing, increase the EM-robustness of the layout with the overall goal of reducing the negative impact of EM on the circuit’s reliability.
A procedural approach to automate the manual design process in analog integrated circuit design
(2018)
This paper presents a novel approach to automating the design of analog integrated circuits: (1) the Expert Design Plan (EDP), a procedural generator, and (2) the EDP Language, a high-level description language for writing an EDP. An EDP is a parameterizable, executable script, which reproduces a designer’s course of action when designing a circuit. Thus, an EDP formalizes the design expert’s knowledge-based strategy and makes it reusable. Since it is essential that an EDP represents a circuit designers’ way of thinking and working as close as possible, the designers themselves should be enabled to create the EDP. Therefore, our approach provides a input method through a domain-specific language called EDP Language (EDPL). Using this language is intuitive and requires no special training. In an exemplary implementation of our approach, a common-source amplifier is automatically sized using a set of only 10 instructions. Even in the first usage our EDP approach has appeared to be more efficient than the manual sizing process.
Im Vergleich zum digitalen Layoutentwurf weist der analoge Layoutentwurf einen wesentlich geringeren Automatisierungsgrad auf. Dies gilt insbesondere für den Layoutentwurf von Hochfrequenzschaltungen, wo Einflüsse der lokalen Layoutumgebung besonders zu berücksichtigen sind. Bei dieser sog. Kontextabhängigkeit geraten sowohl Optimierungsalgorithmen als auch herkömmliche Generatoransätze schnell an Grenzen. In dieser Arbeit wird eine funktionale Erweiterung des bekannten Generatorprinzips eingesetzt, die es erlaubt, Informationen aus der Layoutumgebung der Instanz in die Layoutgenerierung einzubeziehen. Mit dieser sog. kontextbasierten PCell gelingt die Automatisierung konkreter, bisher nur manuell lösbarer Probleme des Layoutentwurfs von Hochfrequenzschaltungen. Die Arbeit zeigt das Potential kontextbasierter PCells für die weitere Steigerung des Automatisierungsgrades im analogen Layoutentwurf.