Maintaining safe chip and device skin temperatures in small form-factor mobile devices (such as smartphones and tablets) while continuing to add new functionalities and provide higher performance has emerged as a key challenge. This paper presents Therminator, an early stage, fast, full-device thermal analyzer, which generates accurate steady-state temperature maps of the entire smartphone starting from the Application Processor and other key device components, extending to the skin of the device itself. The thermal analysis is sensitive to detailed device specifications (including its material composition and 3-D layout) as well as different use cases (each case specifying the set of active device components and their activity levels). Therminator considers all major components within the device, builds a corresponding compact thermal model for each component and the whole device, and produces their steady-state temperature maps. Temperature results obtained by using Therminator have been validated against a commercial computational fluid dynamics-based tool, i.e., Autodesk Simulation CFD, and thermocouple measurements on a Qualcomm Mobile Developer Platform. A case study on a Samsung Galaxy S4 using Therminator is provided to relate the device performance to the skin temperature and investigate the thermal path design.

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Therminator: a thermal simulator for smartphones producing accurate chip and skin temperature maps

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Heat Transfer Thermal Management Of Electronics

Recently, augmented reality technologies are debuting to the mainstream markets. Currently, smartphones are still the dominant computing platform for providing mobile augmented reality (MAR) experience to users. Nevertheless, MAR on smartphones faces some key challenges as the rich functionalities increase concerns including battery power drain and thermal dissipation. This paper takes the first step to understand the characteristics of MAR apps from the system and architecture perspectives. We observe that MAR apps exhibit much higher Thread-level Parallelism (TLP) and have a potential to better exploit the big.LITTLE architecture. We further build Multi-comPonent Power and Thermal Analysis Tool (MPPTAT) on Android mobile platform to break down the power consumption and thermal dissipation into hardware components, threads, and phases. Power characterization shows that MAR apps exhibit higher energy consumption compared to other popular mobile non-MAR apps, mainly contributed by the camera. Thread level breakdown shows that the major CPU power consumption is MediaServer, which controls the camera to capture and process multimedia information on smartphones. We also investigate the impact of DVFS, and results indicate that current MAR apps' implementations are mostly CPU intensive and the mobile GPU is not well exploited yet. Moreover, thermal characterization shows that compared to the non-MAR apps, the frequent usage of the camera not only increases the temperature of the camera module but also dissipates the heat to other layers, which directly affect user experience. We believe that our work reveals insights for software and hardware designers to fine-tune augmented reality workloads on mobile platforms.

Understanding the Characteristics of Mobile Augmented Reality Applications

Purpose




This paper aims to present a robust prediction method for estimating the quality of electronic products assembled with pin-in-paste soldering technology. A specific board quality factor was also defined which describes the expected yield of the board assembly.




Design/methodology/approach




Experiments were performed to obtain the required input data for developing a prediction method based on decision tree learning techniques. A Type 4 lead-free solder paste (particle size 20–38 µm) was deposited by stencil printing with different printing speeds (from 20 mm/s to 70 mm/s) into the through-holes (0.8 mm, 1 mm, 1.1 mm, 1.4 mm) of an FR4 board. Hole-filling was investigated with X-ray analyses. Three test cases were evaluated.




Findings




The optimal parameters of the algorithm were determined as: subsample is 0.5, learning rate is 0.001, maximum tree depth is 6 and boosting iteration is 10,000. The mean absolute error, root mean square error and mean absolute percentage error resulted in 0.024, 0.03 and 3.5, respectively, on average for the prediction of the hole-filling value, based on the printing speed and hole-diameter after optimisation. Our method is able to predict the hole-filling in pin-in-paste technology for different through-hole diameters.




Originality/value




No research works are available in current literature regarding machine learning techniques for pin-in-paste technology. Therefore, we decided to develop a method using decision tree learning techniques for supporting the design of the stencil printing process for through-hole components and pin-in-paste technology. The first pass yield of the assembly can be enhanced, and the reflow soldering failures of pin-in-paste technology can be significantly reduced.

Optimising pin-in-paste technology using gradient boosted decision trees

Recently, mobile applications have gradually become performance- and resource- intensive, which results in a massive battery power drain and high surface temperature, and further degrades the user experience. Thus, high power consumption and surface over-heating have been considered as a severe challenge to smartphone design. In this paper, we propose DTEHR, a mobile Dynamic Thermal Energy Harvesting Reusing framework to tackle this challenge. The approach is sustainable in that it generates energy using dynamic Thermoelectric Generators (TEGs). The generated energy not only powers Thermoelectric Coolers (TECs) for cooling down hot-spots, but also recharges micro-supercapacitors (MSCs) for extended smartphone usage. To analyze thermal characteristics and evaluate DTEHR across real-world applications, we build MPPTAT (Multi-comPonent Power and Thermal Analysis Tool), a power and thermal analyzing tool for Android. The result shows that DTEHR reduces the temperature differences between hot areas and cold areas up to 15.4°C (internal) and 7°C (surface). With TEC-based hot-spots cooling, DTEHR reduces the temperature of the surface and internal hot-spots by an average of 8° and 12.8mW respectively. With dynamic TEGs, DTEHR generates 2.7-15mW power, more than hundreds of times of power that TECs need to cool down hot-spots. Thus, extra-generated power can be stored into MSCs to prolong battery life.

https://dl.acm.org/doi/pdf/10.1145/3173162.3173188

Exploiting Dynamic Thermal Energy Harvesting for Reusing in Smartphone with Mobile Applications

The presented work analyses the influences that different materials, component placements and other factors have on the efficiency of heat dissipation from the main processing unit of embedded devices. Passive cooling methods are investigated, since these not only require the least space, but also have no need to be powered. It has been shown, with the aid of simulations implemented in SolidWorks, that these devices can be efficiently cooled by an optimal selection of manufacturing materials and using the constructive elements of these devices, such as the EMC shield to draw away heat from components.

Thermal management of embedded devices

Thermal management in electronics continues to be one of the most important fields which can take part in ensuring the reliability of an electronic system. Heat transfer, from a system to the ambient, must be optimized, and even a temperature with 1°C less can have an effect in prolonging the life of an electronic component. The research presented in this paper aims to decrease the junction temperature of a power device, by optimizing the placement of thermal vias underneath the IC. Various thermal vias placement scenarios are analyzed and their benefit in improving the thermal dissipation from a component to the PCB and ambient is discussed.

Thermal via placement for high-power applications

Considering the huge amount of available data, the necessity of high-quality recommendation systems is obvious. The complex and diverse definitions of quality make the problem even harder. Although there are several approaches to address the problem of relevant recommendation according to a given context, they still cannot cover all the involved aspects. In our work, we are proposing an integrated approach that enhances the traditional case-based reasoning approach with domain knowledge in order to improve the quality of our similarity metrics. In order to evaluate our model, we developed a proof-of-concept instance in the touristic domain.

An integrated case-based reasoning and ontology-driven approach for designing recommendation systems

Wireless Sensor Nodes are currently used in fields such as industrial automation, Internet-of-Things, smart marketing, and even medicine, where they are implanted in living organisms. In order to remove all the hassles and design challenges of a battery powered device, the paper proposes an ultra-low power wireless nodes system that can be sustained with thermal energy harvesting alone. Combined with an Augmented Reality application, it is a self-sustainable, eco-friendly and intuitive way of gathering data without requiring maintenance or human intervention. A minimal footprint prototype is presented, along with the steps taken to keep the average current consumption to a minimum while fully maintaining system functionality: dynamically updating connection parameters as required, and optimizing the software running on the Wireless Sensor Nodes. The results of this research are sustained by the simulations, measurements and tests presented in the paper.

Energy Harvesting Wireless Sensor Nodes with Augmented Reality Application Support

Thermal management is one of the most challenging fields, especially in case of high-power electronics. In order to foresee and resolve thermal-related challenges in electronic systems, in each stage of the electronic product development, this paper proposes a V-model based approach of the thermal design to ensure proper thermal management. A case-study is presented that covers different stages of the thermal design process of a generic single-phase smart meter used for both indoor and outdoor operations, for which the thermal V-model approach can be applied successfully.

Alexandra Fodor

Papers

Thermal management of embedded devices

Thermal via placement for high-power applications

An integrated case-based reasoning and ontology-driven approach for designing recommendation systems

Energy Harvesting Wireless Sensor Nodes with Augmented Reality Application Support

Characterization of the V-Model Approach in Thermal Design Process