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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.
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.
The very first International Workshop on Software-intensive Business: Start-ups, Ecosystems and Platforms (SiBW 2018) was held in Espoo (Greater Helsinki), Finland on December 3rd, 2018 – just a day before SLUSH 2018, the world’s biggest startup event. Thanks to the collaboration with the organizers of SLUSH, many of the software-intensive business researchers and practitioners took part also in this event.
The international workshop gathered together 35 registered attendees, from Sweden, Germany, Latvia, Finland, Italy and the Netherlands representing both academia as well as industry. The event itself was sponsored by VTT Technical Research Centre of Finland and the workshop was organized by the newly founded Software-intensive Business research community together with Software Startup Research Network (SSRN).
This research addresses the question of why employees use enterprise social networks (ESN). Against the background of technology acceptance research, we propose an extended unified theory of acceptance and use of technology (UTAUT) model, adapt it to an ESN context, and test our model against data from ESN users of large and medium-sized enterprises. We use partial least squares structural equation modeling to gain insights into the determinants of ESN use. This paper contributes to ESN acceptance research by evaluating a model containing determinants of ESN use. It also examines the effects of determinants on five different usage dimensions of ESN. The results reveal that facilitating conditions are the main driver of ESN use while the impact of intention to use is comparably small. Implications for theory and practice are discussed.
We present a dual active bridge topology suitable for wide voltage range applications covering all combinations of 200V to 600V on the input and 20V to 60V on the output with constant power of 1kW.We employ a stepped inductance scheme to adjust the effective inductance of the converter, thus extending the efficient operation range. Using a variable switching frequency between 35 kHz and 150 kHz with operation-point-dependent limits further increases the performance of the converter. A prototype was built and the proposed changes have been compared to a fixed frequency, fixed inductance implementation. Measurements show a maximum loss reduction of 40 %, leading to a peak efficiency of 97% while maintaining constant output power over the entire working area.
Many GaN power transistors contain a PN junction between gate and the channel region close to the source. In order to maintain the on-state, current must continuously be supplied to the junction. Therefore, the commonly recommended approach uses a gate bias voltage of 12V to compensate the Miller current through a boost circuit. For the same purpose, a novel gate driving method based on an inductive feed forward has been presented. With this, stable turn-on can be achieved even for a bias voltage of only 5V. The effectiveness of this concept is demonstrated by double pulse measurements, switching currents up to 27A and a voltage of 400V. For both approaches a compact design with low source inductance is characterized. In addition to the significant reduction of the gate bias voltage and peak gate current, the new approach reduces the switching losses for load currents >23 A.
This article presents a two-level optimisation approach for the management of controllable and distributed converters with storage systems across different energy sectors. It aims at the reduction of electrical peak load and at the economical optimisation of the electrical energy exchange with the grid, based on a dynamic external incentive, e.g. through dynamic energy price tariffs. By means of a secure, standardised and lean communication with two different internal price signals, an optimal flexibility provision shall be achieved. The two-level optimisation approach consists of a centralised and several distributed decentralised entities. At the centralised level, the distributed flexibilities are invoked for optimal scheduling on the basis of an internal price algorithm for stimulating the decentralised entities. Based on that internal incentive and on the expected demands for electricity, heating and cooling, the decentralised optimisation algorithms provide optimal generation schedules for the energy converters. The suggested interaction between the central and decentral entities is successfully tested and the principle potential for peak shaving and the adaption to dynamic energy-related market prices could be demonstrated and compared to different energy management strategies such as the standard heat-led operation. Further, variations of the system parameters such as load shifting potential, installed capacity and system diversification are evaluated against cost saving potential for the energy supply and overall system performance.
The increasing share of renewable energy with volatile production results in higher variability of prices for electrical energy. Optimized operating schedules, e.g., for industrial units, can yield a considerable reduction of energy costs by shifting processes with high power consumption to times with low energy prices. We present a distributed control architecture for virtual power plants (VPPs) where VPP participants benefit from flexible adaptation of schedules to price forecasts while maintaining control of their operating schedule. An aggregator trades at the energy market on behalf of the participants and benefits from more detailed and reliable load profiles within the VPP.
Reduction of power consumption of digital systems is a major concern especially in modern smart sensor systems. These systems are often only activated on request and their power consumption is therefore dominated by the idle-mode. Power reduction mechanisms such as clock or power gating reduce the activity or leakage in the purely digital circuits. We propose a novel adaptive clocking scheme that optimizes the energy demand using a fine-grained oscillator control on cycle-level. To evaluate our new approach, we analytically analyze the power consumption of the regarded system in comparison with available methods. The power of our new adaptive clocking is shown in an integrated smart sensor for capacitive measurements working in a passive wireless sensor node. Using our methods, we show that the energy demand of the example system is reduced even in the case of continuous measurements that demand for a high activity in the digital circuitry.
Context: Software product lines are widely used in automotive embedded software development. This software paradigm improves the quality of software variants by reuse. The combination of agile software development practices with software product lines promises a faster delivery of high quality software. However, the set up of an agile software product line is still challenging, especially in the automotive domain. Goal: This publication aims to evaluate to what extend agility fits to automotive product line engineering. Method: Based on previous work and two workshops, agility is mapped to software product line concerns. Results: This publication presents important principles of software product lines, and examines how agile approaches fit to those principles. Additionally, the principles are related to one of the four major concerns of software product line engineering: Business, Architecture, Process, and Organization. Conclusion: Agile software product line engineering is promising and can add value to existing development approaches. The identified commonalities and hindering factors need to be considered when defining a combined agile product line engineering approach.
