OnElectronTech https://www.onelectrontech.com Electronics for a better life! Sun, 29 Mar 2020 21:26:34 +0000 en hourly 1 https://wordpress.org/?v=5.3.2 https://www.onelectrontech.com/wp-content/uploads/2018/09/logo_final_large-200dpi-100x100.png OnElectronTech https://www.onelectrontech.com 32 32 MOSFETs for Load Switch Applications https://www.onelectrontech.com/mosfets-load-switch-pmos-nmos-on-resistance-inrush-current-applications/?utm_source=rss&utm_medium=rss&utm_campaign=mosfets-load-switch-pmos-nmos-on-resistance-inrush-current-applications https://www.onelectrontech.com/mosfets-load-switch-pmos-nmos-on-resistance-inrush-current-applications/#respond Sun, 29 Mar 2020 21:16:10 +0000 https://www.onelectrontech.com/?p=2418 Today, as many electronic devices are becoming mobile and portable, low power consumption is one of the most important requirements. We need carefully select the circuits and components for efficient …

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Today, as many electronic devices are becoming mobile and portable, low power consumption is one of the most important requirements. We need carefully select the circuits and components for efficient power management to extend the battery life. In many electronic devices, the loads normally are not always on or off, but working based on a pattern, or duty cycle. A temperature may only need to sample the environment once every minute, while the RF module only needs to turn on when the data is ready for transmission. Therefore, we may want to turn the power off to these modules when they really don’t need the power at the moment and then turn them on when they need. This is when the load switches are used based on the designated duty cycles of their applications. The load switches have been widely used in laptops, smart phones, IoT (Internet of Things) devices and hand-held portable device. The basic form of a load switch is shown below:

Figure 1 Example PMOS Load Switch

As shown in the above figure, the load switch is placed in between of the power source and the load. The load switch is controlled by the microcontroller that decides when to turn on/off the load switch to connect or disconnect the load to the power rail. The core of a load switch is the pass transistor that is often a MOSFET to pass the power supply to the load to be controlled.

How to select the load switch to be used in a specific application is determined by many factors, including the cost, form factor, functionality and availability. All of these factors may change from application to application. Therefore, we cannot rely o them to give a common guidelines on how to select the load switches. If we decide the selection based on the functionalities of the load switch, then we can make the good selections applicable to all scenarios.

The load switch is used to turn on/off the loads. Therefore, its performance is determined by the two distinct periods, the ON time and OFF time. When the load switch is turned off, the key parameter to affect the system is the quiescent current that contributes to the standby power consumption and the turn-off characteristics.

When the load switch is on, it becomes part of the load of the power supply. Therefore, its own characteristics can affect the overall performance of the system, which includes the ON resistance, control topology, gate-to-source voltage, inrush current and turn on speed, etc.

ON resistance RDS(on)

The on resistance of a MOSFET is the resistance of the channel between the drain and source of the MOSFET when it is in conducting operation. The higher the on resistance, the more power loss it causes. According to semiconductor physics, the on resistance of MOSFETs is decided by the dimensions of the device channel and the type of the material forming the channel. Traditionally, P-channel MOSFET cannot reach the same RDS(on)  performance of an N-channel MOSFET with the same die size. The majority carriers in an N-channel MOSFET are electrons, whose mobility is approximately 2 to 3 times higher than that of a P-channel. To achieve the same level of RDS(on), the P-channel die must be made 2 to 3 times larger than the N-channel. With the larger die size, we can gain the advantages of lower thermal resistance and higher current rating for the P-channel MOSFET, but lose dynamic performance due to the larger gate capacitance. Generally, we make selections between the P-channel and N-channel based on the specific requirements of applications.

Low-frequency applications

For low-frequency applications, the conduction loss caused by the load switch is the primary concern. Therefore, RDS(on)  is the only requirement matters the performance. So we can select P-channel MOSFET over N-channel MOSFET. The P-channel MOSFET will have larger chip size to provide a low RDS(on). Since the working frequency is low, e.g., from 10 to 50k Hz, the loss in dynamic performance due to the dimensions of device structure is not significant. On the other side, we gain advantages of better thermal performance and higher current rating.

High-frequency applications

For high-frequency applications, e.g., high frequency PWM, the dominant loss is caused by the switching operation. An N-channel MOSFET with the same chip size as a P-channel MOSFET has comparable dynamic and thermal performance due to the similar gate charges and structural dimensions, but the P-channel MOSFET has a larger RDS(on) due to the much lower mobility rate. Selecting between the P-channel and N-channel MOSFET is totally based on the RDS(on)  and gate charge QG.

Mobile applications

Other than the distinct separation between the low- and high- frequency applications, we have the popular mobile applications, which have strict requirements for the size. Normally, such applications demand a power less than 10W. For such applications, the design goal is to achieve high efficiency while keeping the size acceptable according to the designs. Fast switching frequencies can help reduce the size of the inductors. Moreover, we can use the P-channel MOSFET that eliminates the need for extra gate driver circuitry to further save space.

Figure 2 NMOS Load Switch Control Circuit
Figure 3 PMOS Load Switch Control Circuit

Gate-to-source voltage, VGS

As we have mentioned, the on resistance RDS(on) between the drain and source is one of the most important characteristics of the MOSFET. The gate-to-source voltage determines the RDS(on). The MOSFET turns on when the applied gate-to-source voltage is higher than the threshold voltage, Vth.

According to theory of MOSFET, the relationship between the on resistance RDS(on)  and the gate-to-source voltage VGS is

Figure 4 The on-resistancde RDS(on) vs. the gate-to-source voltage VGS of a MOSFET (By On Semiconductor)

We know the MOSFET’s “on state” consists of a triode region and saturation region. The triode region is where the above formula refers to and the resistance in the triode region is extremely high. Therefore, the desirable RDS(on) is the value falling in the saturation region as shown in the above plot where the RDS(on) begins to flatten out.

Turn on characteristics

When the load switch is turned on, its dynamic characteristics affect the overall performance of the system, such as the inrush current and turn-on speed. The inrush current is caused by the capacitive loading characteristics of the load when the power is applied to it. The capacitive load starts to charge and it appears to have no resistance. The result is a huge current spike and corresponding voltage sag on the power supply output. The slew rate of the voltage being applied to the input capacitance and the capacitance itself determines the inrush current:

IINRUSH = CL x (dv/dt), where CL is the input capacitance and dv/dt is the slew rate of input voltage. As an example, for a 20 uF input capacitor, if the slew rate of the voltage is 4 V/µs, then the inrush current is 20 x (4)  = 80 A. If the power supply, e.g., the battery, has an ESR (equivalent series resistance) of 0.02 Ω, the voltage sag on the battery will be 0.02 x 80 = 1.6 V.

Figure 5 Inrush current and voltage transient on a capacitive load (By Texas Instruments)

This figure shows the effect of inrush current caused by a 100µF load capacitance. Without any slew rate control, the inrush current peaks at about 6.88A and forces the voltage rail to drop from 3.3V down to 960mV. (By Texas Instruments)

The effect of the inrush current can be mitigated by specifying a settling time during which the system is kept in a reset state until the voltage stabilizes. Different load switches have different methods for reducing the slew rate. The following circuit uses an external resistor R1 an capacitor C1 to reduce the turn on speed of the pass transistor.

Figure 6 PMOS Load Switch – select the components of the control circuit

In the circuit, the R1 and R2 form a voltage divider to determine the gate voltage and they can be determined by using the following equation:

VSG,MAX can be found in the datasheet. To calculate R1, we can use a value between 1k and 10kΩ for R2. The C1 combining with R1 determines the turn-on speed of the pass transistor and it can be calculated. The selection of R1, R2 and C1 is critical to the performance of the load switch. C1 must be much larger than the gate capacitance of the pass transistor to be able to control the slew rate.

Where the plateau voltage VPL is defined as:

Figure 7 The drain current ID vs. the gate-to-source voltage VGS of a MOSFET (By On Semiconductor)

As important as the inrush current to the performance of load switches, the turn-on speed is one of the key parameters to be considered in selection of the right load switch for your applications. Fast turn-on speed can cause inrush current. A slower turn-on speed is helpful for reducing the input current spikes but may cause other problems. As shown in the above figure, MOSFETS operates at different temperatures show different characteristics but all the transfer curves for different temperatures will intersect at a specific VGS, which is called the inflection point. Above the inflection point, RDS(on) increases as temperature increases, so the current decreases. For parallel power distribution, the current decreases in one branch will cause the current increases in less resistive branch. Therefore, the final result is a balanced current distribution among all the loads. Below the inflection point, the MOSFET behaves as a BJT (Bipolar Junction Transistor) that conducts higher current as it’s heated up and the current continuously increases as temperature rises. This is the situation normally leads to the thermal runaway of the device. The selection of the appropriate gate-to-source voltage is very important to keep the load switch from thermal runaway.

Texas Instruments load switches feature a controlled rise time, and for some products, the rise time can be adjusted. The rise time of the load switches can be increased by adding an external capacitor between the CT pin and GND pin as shown below in the application circuit of TPS22965.

Figure 8 TI TPS22965 with slew rate control (By Texas Instruments)

The following two figures show the different rising times of TPS22965 load switch for different CT capacitance used for a 22 µF capacitive load.

Figure 9 The rise time of output voltage and the inrush current peak when no capacitor added to the CT pin of TPS22965 (By Texas Instruments)

This figure shows the rise time and the inrush current of the load switch when there is no additional capacitance is added to the CT pin of TPS22965. The rise time is so short that it may not be sufficient to limit the inrush current to the desired peak value. The figure indicates the rise time is about 120µs and the peak current is above 650mA.

Figure 10 The rise time of output voltage and the inrush current peak when 150pF capacitor added to the CT pin of TPS22965 (By Texas Instruments)

This figure shows the rise time and the inrush current of the load switch when there is an additional 150pF capacitance is added to the CT pin of TPS22965. The rise time is so short that it may not be sufficient to limit the inrush current to the desired peak value. The figure indicates the rise time is extended to about 320us and the peak current is reduced to just about 360mA.

 

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EPTC 2020 – 22nd Electronics Packaging Technology Conference https://www.onelectrontech.com/eptc-2020-electronics-packaging-technology-conference-interconnection-materials-processing/?utm_source=rss&utm_medium=rss&utm_campaign=eptc-2020-electronics-packaging-technology-conference-interconnection-materials-processing https://www.onelectrontech.com/eptc-2020-electronics-packaging-technology-conference-interconnection-materials-processing/#respond Tue, 17 Mar 2020 01:41:54 +0000 https://www.onelectrontech.com/?p=2413 When: December 2 – 4, 2020 Where: Singapore The 22nd Electronics Packaging Technology Conference (EPTC) is an International event organized by the IEEE RS/EPS/EDS Singapore Chapter and sponsored by the …

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  • When: December 2 – 4, 2020
  • Where: Singapore
  • The 22nd Electronics Packaging Technology Conference (EPTC) is an International event organized by the IEEE RS/EPS/EDS Singapore Chapter and sponsored by the IEEE Electronics Packaging Society (EPS). Since its inauguration in 1997, EPTC has developed into a highly reputed electronics packaging conference in the Asia-Pacific region and is well attended by experts in all aspects of packaging technology from all over the world. It is a major forum for the exchange of knowledge and experience in electronics packaging and provides opportunities to network and meet with leading international experts. The technical sessions cover the whole range of electronics packaging including the following areas:

    • Advanced Packaging: Advanced Flip-chip, 2.5D & 3D, PoP, embedded passives & actives on substrates, System in Packaging, embedded chip packaging technologies, Panel level packaging, RF, Microwave & Millimeter-wave, Power and Rugged Electronics Packaging etc.
    • TSV/Wafer Level Packaging: Wafer level packaging (Fan in/Fan out), embedded chip packaging, 2.5D/3D integration, TSV, Silicon & Glass interposer, RDL, bumping technologies, etc.
    • Interconnection Technologies: Au/Ag/Cu/Al Wire-bond / Wedge bond technology, Flip-chip & Cu pillar, solder alternatives (ICP, ACP, ACF, NCP, ICA), Cu to Cu, Wafer level bonding & die attachment (Pb-free) etc.
    • Emerging Technologies: Packaging technologies for MEMS, biomedical, optoelectronics, Internet of things, photo voltaic, printed electronics, wearable electronics, Photonics, LED, etc.
    • Materials and Processing: advanced materials such as 2D materials, photoresist, polymer dielectrics, solder materials, die attach, underfill, Substrates, Lead-frames, PCB etc for advanced packaging, and assembly processes using advanced materials
    • Equipment and Process Development & Automation: processes development, equipment automation, process and equipment hardware improvements, data analytics, in-situ metrology.
    • Electrical Simulation & Characterization: Power plane modeling, signal integrity analysis by simulations and characterization. 2D/2.5D/3D package level high-speed signal design, characterization and test methodologies.
    • Mechanical Simulation & Characterization: Thermo-mechanical, moisture, fracture, fatigue, vibration, Shock and drop impact modeling, chip-package interaction, etc.
    • Thermal Characterization & Cooling Solutions: Thermal modeling and simulation, component, system and product level thermal management and characterization
    • Quality, Reliability & Failure Analysis: Component, board, system and product level reliability assessment, Interfacial adhesion, accelerated testing, failure characterization, etc.

    Read more at: https://eps.ieee.org/conferences.html

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    EWTS 2020 – The 7th Enterprise Wearable Technology Summit https://www.onelectrontech.com/ewts-enterprise-wearable-technology-summit-2020-ar-vr/?utm_source=rss&utm_medium=rss&utm_campaign=ewts-enterprise-wearable-technology-summit-2020-ar-vr https://www.onelectrontech.com/ewts-enterprise-wearable-technology-summit-2020-ar-vr/#respond Tue, 17 Mar 2020 01:01:25 +0000 https://www.onelectrontech.com/?p=2407 When: October 20 -22, 2020 Where: San Diego, CA BrainXchange’s Enterprise Wearable Technology Summit (EWTS) is where enterprises go to innovate with AR, VR, and wearables. The EWTS is the …

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  • When: October 20 -22, 2020
  • Where: San Diego, CA
  • BrainXchange’s Enterprise Wearable Technology Summit (EWTS) is where enterprises go to innovate with AR, VR, and wearables. The EWTS is the longest-running and most comprehensive event dedicated to the business and industrial applications for wearables; including smart glasses and other HMDs, Augmented, Virtual, and Mixed Reality, body-worn sensors, wrist wearables, and exoskeletons.

    Application Focus:

    Application focus of BrainXchange’s EWTS (Enterprise Wearable Technology Summit) 2020, October, 20 – 22, 2020, San Diego, CA

    Industries Covered:

    The industries covered by EWTS (Enterprise Wearable Technology Summit) 2020, October, 20 – 22, 2020, San Diego, CA

    Read more at: https://www.brainxchange.com/events/ewts-2020/event-home

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    IQT New York 2020 (Inside Quantum Technology NEW YORK 2020) https://www.onelectrontech.com/inside-quantum-technology-iqt-new-york-2020-conference-computing-networking-sensors/?utm_source=rss&utm_medium=rss&utm_campaign=inside-quantum-technology-iqt-new-york-2020-conference-computing-networking-sensors https://www.onelectrontech.com/inside-quantum-technology-iqt-new-york-2020-conference-computing-networking-sensors/#respond Tue, 10 Mar 2020 02:44:46 +0000 https://www.onelectrontech.com/?p=2397 IQT New York 2020 showcases the future of quantum computing, quantum networking and quantum sensors where quantum tech means business. When: July 8 – 9, 2020 Where: Convene Conference Center, …

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    IQT New York 2020 showcases the future of quantum computing, quantum networking and quantum sensors where quantum tech means business.

    • When: July 8 – 9, 2020
    • Where: Convene Conference Center, New York, USA

    Quantum technology is developing rapidly, from fascinating science projects to use -cases to business development. Some quantum technologies are already earning money. According to Inside Quantum Technology’s research division, Quantum Key Distribution (QKD), will reach revenues of around $140 million in 2020 and the market for Quantum Computers will reach approximately $110 million at about the same time. Each quantum technology market segment is expected to go on to be worth millions of dollars annually within a few years. Quantum technology now embraces quantum computing, QKD, post-quantum cryptography (PQC) and quantum sensors; and of course, all the related software.

    Exhibitors/Sponsors

    IQT New York 2020 Exhibitors/Sponsors 1

    IQT New York 2020 Exhibitors/Sponsors 2

    Media Sponsors

    IQT New York 2020 Media Sponsors

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    Sensors USA 2020 – Commercialization of Disruptive Sensor Technology https://www.onelectrontech.com/conferencde-sensor-usa-2020-commercialization-of-disruptive-sensor-technology/?utm_source=rss&utm_medium=rss&utm_campaign=conferencde-sensor-usa-2020-commercialization-of-disruptive-sensor-technology https://www.onelectrontech.com/conferencde-sensor-usa-2020-commercialization-of-disruptive-sensor-technology/#respond Tue, 10 Mar 2020 01:48:10 +0000 https://www.onelectrontech.com/?p=2393 When: November 18 – 19, 2020 Where: Santa Clara Convention Center, Santa Clara, CA, USA Sensors USA 2020, the 2-day conference and exhibition will bring together material suppliers, component providers, …

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  • When: November 18 – 19, 2020
  • Where: Santa Clara Convention Center, Santa Clara, CA, USA
  • Sensors USA 2020, the 2-day conference and exhibition will bring together material suppliers, component providers, sensor manufacturers and end users. With a program selected by analysts and industry experts, Sensors USA is the place to find the technologies and business partners to create the most innovative products.

    Wide range of industries:

    • Consumer electronics
    • Automotive / Transportation
    • Healthcare & Medical
    • Industrial manufacturing
    • Telecom

    What you will learn at Sensors USA 2020?

    • Needs from component providers, end users and system integrators
    • What are the most disruptive sensor technologies
    • The key players in wearable sensors market
    • Applications in the Internet of Things (IoT)
    • New applications and innovations in sensors
    • Sensor manufacturing with printed electronics and beyond.
    • New materials for sensors (graphene, carbon nanotubes, e-textile, transparent conductive films, etc…)
    Sensors USA 2020 Show Timetable

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    AI (Artificial Intelligence) Chips – A Brief Introduction To Seven Popular Products https://www.onelectrontech.com/ai-artificial-intelligence-chips-neural-network-processing-unit/?utm_source=rss&utm_medium=rss&utm_campaign=ai-artificial-intelligence-chips-neural-network-processing-unit https://www.onelectrontech.com/ai-artificial-intelligence-chips-neural-network-processing-unit/#respond Mon, 09 Mar 2020 02:42:17 +0000 https://www.onelectrontech.com/?p=2381 AI – Artificial Intelligence has run into the expressway and poised as one of the most promising technologies in this decade. Currently, AI has become very sophisticated and been demanding …

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    AI – Artificial Intelligence has run into the expressway and poised as one of the most promising technologies in this decade. Currently, AI has become very sophisticated and been demanding more computing power for realizing better algorithms. The carrier of AI, silicon chips is the bottle neck of providing the ultimate computing power. AS demanded by AI tasks, new hardware that is specially designed for specialized AI software, is needed for quick training of the neural network and reduce the power consumption. The Moore’s Law of Semiconductor industry has driven the process to its limit. We cannot reduce the chip size and put more transistors in the silicon die because quantum tunneling effect prevails when the device gate shrinks to below 5nm.The technology behind AI can be a critical role in driving the economy to grow as AI has been eyed as the pivot part of many products, such as self-driving cars, robotics, smart homes, industrial controls and many others. AI technology consists of Algorithms, processing power and data, among which the computing power depends on the hardware, AI chips. Currently, there are hundreds of companies are involved in the manufacturing and research of AI chips. As AI turns out the mainstream technological trend, more and more companies will join this war for the AI market. The AI chips are still in their starting stage, we have not seen any all-in-one type of devices. AI chips are application oriented thus we see many different types of AI chips in the market for various applications, which include GPU (Graphic Processing Unit), NPU (Neural Processing Unit), TPU (Tensor Processing Unit), Amazon AI chip for Alexa home assistant, Apple AI chip for Siri and FaceID and Tesla AI chip for self-driving electric cars. From all of those in the market, let’s take a look at seven of them.

    Qualcomm Cloud AI 100

    Qualcomm Cloud AI 100 Chip

    Qualcomm Inc. launched the new Cloud AI 100 chip that is capable of translating audio input into text-based requests with an AI algorithm trained by massive amount of data. The Qualcomm Cloud AI 100 is considered as the solution that is designed to meet cloud AI inferencing needs for datacenter providers. The AI chip is built from the ground up to help accelerate AI experiences, providing a turn-key solution that addresses the most important aspects of cloud AI inferencing – including low power consumption, scale, process node leadership, and signal processing expertise, which facilitates the ability of datacenters to run inference on the edge cloud faster and more efficiently.

    • Low power consumption
      • 10x performance improvement over available AI inference accelerator solutions
    • Process node leadership
      • Built from the ground up with 7nm process node, performance and power leadership
    • Scale
      • 700+ million Qualcomm Snapdragon chipset shipped per year
      • Billions of discrete chipsets per year
      • Work with over 30 different fabs across the world
    • Signal Processing expertise
      • Power-efficient signal processing expertise across major areas:
        • Artificial Intelligence
        • eXtended Reality
        • Camera
        • Audio
        • Video
        • Gestures

    Apple Bionic A13

    Apple Bionic A13 AI Chip

    Apple Bionic A13 Processor in iPhone 11, 11 Pro and 11 Pro Max boosts new Apple iPhones performance by at least 20% and reduces power consumption by 30% over the last generation. Apple Bionic A13 are fabricated on TSMC’s N7P process. It’s integrated with 8.5 billion transistors and features two Lightning cores at 2.65 GHz and four Thunder high-efficiency cores at 1.8 GHz. Besides these 6 cores, it also contains a quad-core GPU and an octa-core neural engine that is dedicated to the hardware accelerators for machine learning that makes the chip six time faster on matrix multiplication. Apple Bionic A13 has two built-in machine learning accelerators that can handle one trillion operations per second.

    Google Tensor Processing Units TPU

    Google Unveils tiny new AI chips for on-device machine learning – by The Verge

    Google’s Edge TPU (Tensor Processing Units) is made to perform machine learning in miniature devices, like IoT (Internet of Things) devices. This AI chip is capable of carrying out inference based on the machine learning algorithm trained by datasets. The new Google Edge TPU chips are even smaller compared to a US Penny.

    Tensor Processing Units (TPUs) are Google’s custom-developed application-specific integrated circuits (ASICs) used to accelerate machine learning workloads. TPUs are built on the foundation of Google’s expertise in machine learning. The TPUs that Google makes for cloud enables us to run machine learning workloads on Google’s TPU accelerator hardware using TensorFlow. Google Cloud TPU is designed for maximum performance and flexibility to help researchers, developers, and businesses to build TensorFlow compute clusters that can leverage CPUs, GPUs, and TPUs. High-level TensorFlow APIs help you to get models running on the Cloud TPU hardware.

    The TPU resources accelerate the performance of linear algebra computation, which is used heavily on machine learning applications. TPUs minimize the time-to-accuracy when training large, complex neural network models that can converge in hours now on TPUs instead of weeks on other hardware platforms.

    Microsoft Project Brainwave – demonstrated using Intel’s 14 nm Stratix 10 FPGA

    Microsoft unveils Project Brainwave for real-time AI with Intel Stratix-10

    Microsoft Project Brainwave is a cloud-based deep learning platform for real-time AI inference in the cloud and on the edge. A soft Neural Processing Unit (NPU) that is based on a FPGA (Field Programmable Gate Array) like Intel 14 nm Stratix 10 is used to accelerate Deep Neural Network (DNN) inferencing. The applications of Project Brainwave outcomes include computer vision and natural language processing. Project Brainwave is transforming computing by augmenting CPUs with an interconnected and configurable compute layer composed of programmable silicon.

    According to Microsoft, the FPGA configuration achieved more than an order of magnitude improvement in latency and throughput on RNNs (Recurrent Neural Networks) for Bing, with no batching. The software overhead and complexity are greatly improved due to the real-time AI processing and ultra-low latency without batching required. Microsoft Project Brainwave has also been applied on the cloud with Azure Machine Learning and the edge with Azure DataBox Edge.

    Samsung Exynos 9820 SoC

    Samsun Exynos 9820 Neural Processing Unit based Tri-cluster CPU for AI Applications

    Samsung has developed its NPU (Neural Processing Unit) based AI chips for deep learning algorithms which are the core element of artificial intelligence (AI) as this is the process that can be utilized by computers to think and learn as a human being. Samsung’s Exynos 9820 is built to maximize intelligence on the go to offer powerful AI capabilities with NPU and the performance through tri-cluster CPU. This AI chip includes On-Device AI lightweight algorithms that directly compute and process data from within the device itself. The latest algorithm solution is over 4 times lighter and 8 times faster than existing algorithms.

    The Exynos 9820 pushes the limit of mobile intelligence with an integrated Neural Processing Unit (NPU), which specializes in processing artificial intelligence tasks. It allows the processor to perform AI-related functions seven times faster than its predecessor. From enhancing photos to advanced AR (Augmented Reality) features, the Exynos 9820 with NPU expands AI capabilities of mobile devices.

    The Samsung Exynos has two 4th generation custom CPUs, two Cortex-A75 cores and four Cortex-A55 cores. This tri-cluster architecture with intelligence task scheduler boosts multi-core performance by 15 percent compared to the last generation. The 4th generation custom CPU with enhanced memory access capability and cutting-edge architecture design improves single core performance by up to 20 percent or boosts power efficiency by up to 40 percent.

    Huawei Ascend 910 AI Processor

    Huawei Ascend 910 High-performance AI chip

    Huawei Ascend 910 boasts the world’s most powerful AI processor. Ascend 910 delivers 256 Teraflops @ FP16 for half-precision floating point operations and 512 Teraflops @ INT8 for integer (INT8) precision calculation. Also, Ascend 910 consumes only 310W at full load. The performance improvement is due to Huawei’s own Da Vinci architecture. Ascend 910 is a high-integration SoC processor. Inaddition to the Da Vinci AI cores, it integrates cPUs, DVPPm and Task Scheduler. It self-manages to make full use pf its high computing power. At the same time, Huawei introduced its MindSpore, all-scenario AI computing framework that supports development for AI applications. In a typical training session based on ResNet-50, the combination of Ascend 910 and MindSpore is about two times faster at training AI models than other mainstream training cards using TensorFlow.

    Baidu Kunlun AI Chip based on Baidu XPU Neural Processing Architecture

    Baidu new AI Chip – Kunlun based on Baidu’s XPU Neural processing Architecture

    Baidu Kunlun AI chip is designed for cloud, edge and AL applications. Samsung Foundry will mass produce Kunlun AI chips with 14 nm process and use the Interposer-Cube 2.5D packaging structure in early 2020. The Baidu Kunlun AI accelerator is based on Baidu’s own XPU processor architecture that includes thousands of small cores for many cloud or edge applications. According to Baidu, Kunlun boasts 512 Gbps memory bandwidth with two HBM2 (High Bandwidth Memory 2) memory packages and provides up to 260 TOPS at 150W power consumption. Kunlun will carry Baidu’s natural language processing framework, Ernie to process language boasting three times faster than traditional GPUs.

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    MIT Developed Localizing Ground-Penetrating Radar (LGPR) for Self-Driving Vehicles https://www.onelectrontech.com/mit-lincoln-laboratory-and-wavesense-localizing-ground-penetrating-radar-lgpr-for-self-driving-vehicles/?utm_source=rss&utm_medium=rss&utm_campaign=mit-lincoln-laboratory-and-wavesense-localizing-ground-penetrating-radar-lgpr-for-self-driving-vehicles https://www.onelectrontech.com/mit-lincoln-laboratory-and-wavesense-localizing-ground-penetrating-radar-lgpr-for-self-driving-vehicles/#respond Fri, 21 Feb 2020 02:11:13 +0000 https://www.onelectrontech.com/?p=2374 MIT Lincoln Laboratory Has developed the LGPR (Localizing Ground-Penetrating Radar) technology that can be commercially available for helping autonomous vehicles navigate by using subsurface geology. LGPR sends pulses of electromagnetic …

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    MIT Lincoln Laboratory Has developed the LGPR (Localizing Ground-Penetrating Radar) technology that can be commercially available for helping autonomous vehicles navigate by using subsurface geology. LGPR sends pulses of electromagnetic waves to detect objects in the soil layers, rocks and road bedding by the reflection from them and use the detected features to accurately position vehicles up to centimeter-level accuracy. Vehicles equipped with LGPR can find lanes in the road that is covered by snow or dust or in the fog. LPGR technology can significantly impact the self-driving vehicle industry. Currently, most autonomous vehicles use optical sensors to detect the road surface and surrounding infrastructure to keep them in the lane. But optical systems depend on weather conditions. The lane markings and the road surfaces can be impossible to detect if covered by snow or dust using current optical systems.

    Localizing Ground-Penetrating Radar (LGPR) uses relatively stable subsurface features and their geolocation to locate the vehicle even in adverse weather conditions – MIT Lincoln Laboratory

    The LPGR sensors are used to generate a baseline map of a road’s subsurface by recording the reflections of underground objects scanned by high-frequency radar signals. Then, an LGPR equipped vehicle passes a point in the baseline map and scans the road’s subsurface. The LGPR system compares the real-time data against the baseline map and creates an estimate of the vehicle’s location, which can be within a few centimeters in real-time and at highway speed, even at night in snowstorm. The concept of LGPR was first introduced at the 2017 Automated Vehicles Symposium from July 11 to July 13 in San Francisco by Stanley and David Cist, vice president of R&D at GSSI Geophysical Survey Systems, Inc.

    LGPR – A localizing ground-penetrating radar system from the MIT Lincoln Laboratory complements existing technology – WaveSense

    Studies show evidence that the deep subsurface features mapped by LGPR should be relatively immune to changes on the road surface, which often compromise traditional optical sensors. The researchers at MIT Lincoln Laboratory have demonstrated how stable the subsurface features can be compared to the road surfaces as shown by the figure below. The real-time tracking data taken during a snowstorm is almost the same as the baseline data recorded by different equipment on another day. This is why LGPR can be very accurate in real-time position sensing.

    MIT Lincoln Laboratory – baseline map and the real-time tracking data show almost identical features

    Compared to most sensor based on optical technologies, LGPR shows advantages:

    • LPGR is robust under various difficult conditions that affect GPS, Lidar or camera sensors, like in-tunnel, canyons, snow, ice, fog, dust, dirt, changing light and dynamic environment,
    • LGPR is very independent of the changes to the road surfaces or in the dynamic environment, where the landmarks are damaged or changed, or obscured, or the road markings fade, or traffic signs are moved.
    • LGPR provides stable subsurface mapping that reduces the need for continual modifications to high-resolution road maps.

    It was estimated that mass-produced LGPR devices would cost no more than $300 each because of its simple design. With this low cost, LGPR can be a useful addition to the existing sensor suites and make autonomous vehicles safer and more capable of dealing with challenges from adverse environment and dynamic road conditions.

    WaveSense, a MIT spinoff startup is commercializing LGPR technology for self-driving vehicles. With this technology, the arrival of self-driving vehicles will be accelerated to help save millions of lives and transform the future of transport.

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    Turnigy 9X Repair: A Backwards Battery Blunder https://www.onelectrontech.com/turnigy-9x-repair-a-backwards-battery-blunder/?utm_source=rss&utm_medium=rss&utm_campaign=turnigy-9x-repair-a-backwards-battery-blunder https://www.onelectrontech.com/turnigy-9x-repair-a-backwards-battery-blunder/#respond Thu, 09 Jan 2020 20:14:23 +0000 https://www.onelectrontech.com/?p=2061 D’oh! I knew it’d happen one of these days—I plugged the battery in backwards on my Turnigy 9X transmitter! Now it won’t start up, or even give me the annoying …

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    D’oh! I knew it’d happen one of these days—I plugged the battery in backwards on my Turnigy 9X transmitter! Now it won’t start up, or even give me the annoying “SWITCH ERROR” message! Judging by the distraught R/C forum posts from those whose transmitters had suffered a similar fate, I’m clearly not the only one to have clumsily fumbled the battery in the wrong way.

    What Happened

    You might be wondering how I managed to do it–the AA battery holder that came with the transmitter had a perfectly-matching three-pin connector, as one would expect. It’s polarized and has power on the center pin. That way, it’s nearly impossible to reverse the polarity, even if the connector somehow gets jammed in the wrong way.

    This is how the original battery connector fits. Notice how the polarized connector makes it impossible to put in backwards.

    Problem is, I really didn’t appreciate having to charge AA batteries all the time, so I opted to use a lithium battery pack. Its connector only has two pins, which, while physically compatible, can be plugged in more than one way. And one of those ways connects it in reverse. Strike one.

    The connector on my lithium battery pack. Which pins on the transmitter does it connect to? The left two, or the right two? It isn’t so obvious right away…
    Answer: The second picture is correct. Do the first one and you’ll fry your transmitter!

    I apparently got lucky the first time I installed the battery, since it’s worked fine up until now. Battery life is fantastic with the new lithium pack, so it’s been a while since I last charged it. Ergo, by the time I went around to charging the battery again, I had completely forgotten which pair of pins the battery plugs into! I chose poorly this time. Strike two.

    Then, on top of all that, it didn’t occur to me to double-check before flipping the switch. Strike three.

    At first, nothing of note happened. No smoke or dramatic shower of sparks to make it clear that I had messed up. In fact, it showed neither signs of life, or signs of death. However, after realizing my mistake, it was too late.

    Although the backlight still powers on, the rest of the transmitter is dead.

    Clearly, something inside isn’t working, so the obvious fix would be to take a look inside. A bunch of screws hold the back cover in place, and a bunch more screws hold the various circuit boards in place. I would absolutely recommend having a magnetic screw tray handy!

    Troubleshooting

    All the exciting stuff seems to happen on the big board. Because that’s where all the cables plug into.

    When troubleshooting electronics, you always want to “Follow the Money”. Or, at least, the electrons. Between the battery and whatever tells the screen to display stuff, there’s a chain of components that all have to work perfectly, or else the whole thing doesn’t work at all. Usually, when an electronic component isn’t working properly, there’s only three possibilities:

    1. It’s not receiving power
    2. It’s receiving power but isn’t being told to work
    3. It’s destroyed

    Taking a Closer Look

    I’m hoping I can find the problem early on in the chain, since that intimidating-looking microcontroller with a whole bunch of pins would be pretty difficult to source, let alone replace.

    Nothing looks obviously fried at first glance, so we’ll have to start by checking Number 1: Is everything receiving power? The backlight comes on, so we know that the battery and the power switch are OK.

    Digital electronics usually run on fixed voltages (e.g. 3.3V, 5V), so there’s bound to be a few voltage regulators to check. Sure enough, there’s two on the board. They’re pretty easy to spot, as they don’t have many pins, and have big capacitors nearby.

    Here’s the two regulators on the board. There’s a 5V regulator (right), and a 3.3V regulator (left). They take battery voltage and bring it down to safe levels for the rest of the circuitry.

    Sure enough, the 78L05 seems to be acting up—it’s supposed to output 5 volts. There’s nearly 10 volts at its input, but there’s nothing at its output. After powering down and checking continuity with an ohm meter, it looks like this part is directly connected to the battery. Apparently, it fried when the polarity got reversed, but I’m hoping there’s a chance it spared everything else downstream.

    The 5V regulator seems suspect.

    I confirmed this by directly injecting 5 volts from an external power supply. And sure enough, the transmitter powered right up!

    I’ve never been more excited see this error message!

    The Repair

    According to several forum posts and a video by rcmodelreviews, the 78L05 is a common failure point when the battery is put in backwards. Sometimes, it’ll even explode! Obviously, I’ll need a new one of those. The video also recommends replacing that big orange capacitor next to it, since it might also be damaged. Makes sense—that capacitor is a tantalum capacitor, which are notoriously sensitive to reverse polarity. Even though it looks fine, it might be damaged internally and just waiting for the perfect moment to blow up in a spectacular shower of sparks! (I’m not exaggerating, by the way.)

    I only have a through-hole 78L05—same chip, different package. I also only have through-hole electrolytic capacitors. So, it looks like I’ll have to bodge these in. It won’t look pretty, but the electrons won’t mind.

    The new components from my parts drawer. Spoiler: I eventually needed another 78L05.

    Removing the old components is a pretty simple affair if you have a hot-air station. Otherwise, you can put a bunch of solder on the pins and alternatively heat them with a soldering iron while (gently!) pulling it off the board with tweezers.

    Some surface prep with heat-resistant Kapton tape before blasting the thing with hot air. We’re removing the 78L05 and the big orange capacitor below it. Be more careful than I was–the white connector on the left got a little melty after I accidentally pointed the heat gun at it!

    I’ll have to do a little circuit origami to get the new components into place. Be sure to get the pins in the proper places, or else things might go up in smoke! Fortunately, the through-hole 78L05 has the same pinout and nearly the same pin spacing as its surface-mount variant.

    New components in their new homes.

    Hidden Regulators

    But wait! We’re not done yet! There’s a sneaky little 78L05 tucked under the power switch board, which apparently powers the beeper. Normally, I wouldn’t miss the loud, piercing screams of this cheap plastic speaker, but we might as well fix it while we’re in here, for the sake of completeness.

    Apparently this board has active circuitry on it! The 78L05 lives just underneath the power switch.
    …And replaced. Through-hole components are trickier to desolder, but you can just cut the leads if you’re lazy.

    OK, any other hidden regulators willing to show themselves? Turns out, there’s one more in the wireless module! (It’s in the little box that clips into the back of the transmitter.) However, it seems to be working fine as-is. If your radio is powering on but not transmitting anything, you might want to check this regulator.

    This 78M05 is basically a higher-current version of the 78L05, but apparently more tolerant of reverse polarity?

    A Little Upgrade

    I certainly don’t want to go through this repair all over again, so I’ll add a Schottky diode on the battery connector. It’ll block reverse current, preventing another one of these incidents. I’ll need to break the circuit near the battery connector and bridge the gap with this diode.

    The power input trace, gracefully cut and scraped with a dull box cutter blade. Admittedly not the prettiest repair.

    A Schottky diode works better than a regular diode since it won’t drop the voltage as much. It’ll handle heavy loads better, and the transmitter’s battery gauge will still read close-ish to the correct voltage.

    The diode, soldered into place.

    Conclusion

    These were all the components I replaced

    All in all, this was a rather straightforward repair, and a somewhat inexpensive lesson to remind myself to check battery polarity!

    And everything works now!

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    Low-Power Zero-Drift Op Amp for Precision Applications https://www.onelectrontech.com/low-power-zero-drift-op-amp-for-precision-applications/?utm_source=rss&utm_medium=rss&utm_campaign=low-power-zero-drift-op-amp-for-precision-applications https://www.onelectrontech.com/low-power-zero-drift-op-amp-for-precision-applications/#respond Fri, 20 Dec 2019 03:35:19 +0000 https://www.onelectrontech.com/?p=2357 Op Amps (Operational Amplifiers) have been widely used in many applications since its solid-state monolithic version was invented in 1963. The typical applications of op amps include sensor signal processing, …

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    Op Amps (Operational Amplifiers) have been widely used in many applications since its solid-state monolithic version was invented in 1963. The typical applications of op amps include sensor signal processing, voltage followers, comparators, filters, differentiators, integrators, phase shifters, amplifiers, voltage-to-current converters, etc. Even though op amps have great success in general purpose applications, their usage in areas such as sensors requiring high accuracy face challenges due to some intrinsic limitations, such as input offset voltage and input offset voltage drift.

    Sensors have been used everywhere in our modern life, from consumer, automotive, industrial to medical applications. They have been facing worse environmental challenges than ever, such as EMI (Electromagnetic Interference), ESD (Electrostatic Discharge) spikes, power supply ripples, ground loop errors. On the other hand, in special applications, like wireless sensor network, portable devices, wearables, IoT (Internet of Things) and other battery-powered applications, the signals become small and the rail-to-rail swing is marginal, at the same time, low-power consumption is a pursuit for all applications. All these challenges are ultimately focused on the op amp sitting at the center of the analog portion of the ADC (Analog to Digital Converter).

    When we learned the theories of op amps, they were treated as ideal op amps in textbooks. We established that when the input voltage applied to the input pins was zero volt, the output of the op amp should be nothing but zero volt. In real world we all find that is not true. If we get zero volt on the output pins, we must see non-zero input across the input pins, which we call it Input Offset Voltage and is referred to as VOS. This intrinsic differential voltage stems from the inherent mismatch of the transistors and supporting components in the input section of the op amp during wafer and die fabrication processes. Furthermore, during the process of device packaging, stresses induced by metal-semiconductor bonding can be another contributor to the undesirable mismatch. The combining effect of all factors creates a mismatch of the bias currents flowing through the input circuit of the op amp, resulting in a voltage differential at the input pins.

    In the applications, the differential input voltage VOS is added to the signal’s output voltage by multiplying the closed-loop gain of the op amp, which adds a significant error factor to the output. In the past time, many approaches have been taken to compensate/reduce the VOS effect, such as improved technology on the silicon material processing, better bonding materials, matching components and device trimming procedures. For better performance, zero-drift op amps have been produced by many manufacturers. A zero-drift operational amplifier is an op amp that has reduced input offset voltage and input offset voltage drift with respect to temperature and time. According to the reasons we mentioned before, it is very important to use a zero-drift operation amplifier in applications that require high-accuracy signal amplification.

    Zero-drift op amps contain circuitry that automatically corrects the input offset voltage. This circuitry is categorized as one of the following three types that are referred to as three auto-drift architectures:

    • Auto zero amplifier
    • Chopper amplifier
    • Combined auto-zero and chopper amplifier

    Zero-drift op amps have helped users to achieve the general design specifications like:

    • Temperature offset drift
    • Input offset voltage
    • 1/f noise

    One of the most important sources of ADC (Analog-to-Digital Converter) error is noise. Modern ADC is usually integrated with signal processing circuitry that should filter out completely the noise component in the signal, which is an impossible task. The remaining noise still goes through the conditioning circuitry that tries to amplify not only the signal but also the left-over noise at the same time with the same gain. Therefore, it leads to design low-noise analog front-end to get the optimal SNR (or S/N, Signal to Noise Ratio) for applications requiring high precision. The noise in ADC exists in two types, 1/f noise (also called pink noise or flicker noise) and white noise. It is more prominent in the low-frequency domain (< 100 Hz) due to the irregularities in the conduction path and the bias currents within the input transistors. Compared to BJT (Bipolar Junction Transistor) that were preferred in designing traditional low-noise analog input stage circuit, CMOS input stage design tends to have the noise with a higher amplitude and a higher corner frequency (this the frequency where the pink noise density equals the white noise) than BJT devices. At higher frequencies, 1/f noise is less significant because the white noise from other sources becomes dominant and this is exactly why it is called 1/f noise. This low-frequency 1/f noise can cause big trouble if the input signal is nearly DC like, which is typically observed in the output signals of strain gauges, pressure sensors, thermocouples or any slow-changing signals. The following figure shows how zero-drift topology practically eliminates 1/f noise (Texas Instruments).

    The figure shows how zero-drift topology practically eliminates pink noise (1-f) (Texas Instruments)

    Zero-drift amplifiers provide many benefits to designers, as temperature drift and 1/f noise, always nuisances in the system, are otherwise very difficult to eliminate. In addition, zero-drift amplifiers have higher open-loop gain, power-supply rejection, and common-mode rejection as compared to standard amplifiers; and their overall output error is less than that obtained by a standard precision amplifier in the same configuration.

    On Semiconductor’s NCS333A is a low-power zero-drift op amp features an offset voltage as low as 10 µV over the 1.8V to 5.5V supply voltage range. NCS333A use a chopper-stabilized zero-drift architecture, which helps reduce offset voltage drift over temperature and time.

    On Semiconductor Zero-Drift Op Amp NCS333A Simplified Block Diagram of the Chopper-Stabilized Architecture (Source: On Semiconductor)

    The above Chopper-Stabilized Architecture contains two signal paths. The upper signal path is the feed-forward path that works at higher frequency that extends the gain bandwidth up to 350 kHz. This high frequency feed-forward path retains the high frequency components of the signal and it also improves the closed-loop gain at low frequencies. This offers advantages for low-side current sensing and sensor interface applications where the signal has mainly low frequencies and low differential voltages. The lower path is the feedback path used for sampling the input offset voltage and the feedback is used for correcting the offset at the output. The frequency used for offset correction is 125 kHz. The Chopper-Stabilized architecture is optimized for the best performance up to the Nyquist frequency, which is 62.5 kHz, or 1/2 of the offset correction frequency.

    Features:

    • Gain – Bandwidth Product (GBWP): 350 kHz
    • Low Supply Current: 17 µA (@ 3.3V)
    • Low Offset Voltage (VOS): 10 µV
    • Low Offset Drift: 0.07 µV/°C max
    • Wide Supply Range: 1.8 V to 5.5 V
    • Wide Temperature Range: -40 °C to +125 °C
    • Rail-to-Rail Input and Output

    Applications:

    • Automotive
    • Battery Powered/Portable applications
    • Sensor Signal Conditioning
    • Low Voltage Current Sensing
    • Filter Circuits
    • Bridge Circuits
    • Medical Instrumentation

    On Semiconductor Zero-Drift Op Amp NCS333A Typical Application of Low-Side Current Sensing Circuit (Source: On Semiconductor)

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    Yandex Introduced In-House LiDARs For Their Self-Driving Vehicles https://www.onelectrontech.com/yandex-introduced-in-house-lidar-sensors-for-self-driving-vehicles/?utm_source=rss&utm_medium=rss&utm_campaign=yandex-introduced-in-house-lidar-sensors-for-self-driving-vehicles https://www.onelectrontech.com/yandex-introduced-in-house-lidar-sensors-for-self-driving-vehicles/#respond Wed, 18 Dec 2019 04:21:30 +0000 https://www.onelectrontech.com/?p=2348 Yandex, a technology company creating intelligent products and services based on machine learning has just entered the business of making LiDAR (Light Detection and Ranging) systems for self-driving vehicles. Yandex …

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    Yandex, a technology company creating intelligent products and services based on machine learning has just entered the business of making LiDAR (Light Detection and Ranging) systems for self-driving vehicles. Yandex is currently testing their new LiDAR systems and has planned to use these sensors on their delivery robots too.

     

    Yandex LiDAR systems under test consists of a 360 degree LiDAR on the top and two solid-state LiDARs below

     

    Yandex is working on two types of LiDARs, solid-state LiDAR with 120-degree FOV (Field Of View) for use on the front of the vehicle and a 360-degree LiDAR that can generate the model of the vehicle’s entire environment. The products have been improved based on the extensive three years and over 1.5 million miles of testing. The new LiDAR sensors offer better flexibility that enables us to tune the scanning patterns on the fly. The custom parameters provided with the new versatile software allows the LiDAR to adapt to different driving conditions, such as freeways, dense city streets and diverse weathers. In real world, the Yandex LiDAR sensors can focus on an object while adjusts its scanning pattern to further determine if it is a pedestrian, a bicycle or something else from 200 meters away.

    Surrounding image created by Yandex LiDAR sensors

    Compared to other LiDAR sensors, Yandex engineers receive more information about the vehicle’s surroundings from their own LiDAR sensors from which they can access the raw data. Because of this advantage, Yandex self-driving cars can create a better 3D view of the surroundings via their LiDARs that are synchronized with the data collected by the cameras and RADARs on board. Yandex LiDAR sensors are much cheaper than others. The current prototypes are already half the prices of existing devices. It’s expected to cut the cost of sensors up to 75% when Yandex LiDARs transition to mass production.

    Read more at: https://sdc.yandex.com/#id2

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