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Renesas Electronics Corporation has launched its latest innovation in the automotive semiconductor space: the fifth-generation R-Car X5H system-on-chip (SoC). Designed with 3-nanometer (nm) process technology, the R-Car X5H represents the industry’s first automotive multi-domain SoC built on such an advanced node. This SoC is set to redefine the capabilities of centralized electronic/electrical (E/E) architecture, supporting a range of automotive functions, from advanced driver assistance systems (ADAS) to in-vehicle infotainment (IVI) and gateway applications, all within a single chip.

The R-Car X5H SoC brings unprecedented levels of integration and performance, addressing the growing demand for efficient, powerful, and flexible compute solutions in software-defined vehicles (SDVs). With hardware-based isolation, chiplet extension capability, and extensive AI and graphics processing power, this new SoC series offers automotive original equipment manufacturers (OEMs) and Tier-1 suppliers a comprehensive platform for tackling the complexity of modern vehicle design and functionality.

Unmatched Processing Power and Efficiency

The R-Car X5H delivers AI acceleration of up to 400 TOPS (trillion operations per second) and GPU performance up to 4 TFLOPS, ensuring the SoC can handle demanding tasks in automated driving and infotainment. Featuring 32 Arm Cortex-A720AE CPU cores and six Arm Cortex-R52 dual lockstep CPU cores, this SoC achieves over 1,000K DMIPS for applications and more than 60K DMIPS for real-time processing. Manufactured using Taiwan Semiconductor Manufacturing Company’s (TSMC) 3-nm automotive-grade process, the SoC achieves 30-35% lower power consumption than its 5-nm counterparts. This significant efficiency enhancement not only lowers overall system costs but also extends vehicle range by reducing the need for additional cooling.

Chiplet Extensions for Enhanced Flexibility

A unique feature of the R-Car X5H is its support for chiplet extensions, allowing OEMs to add AI and graphics processing power as needed. Through the Universal Chiplet Interconnect Express (UCle), the SoC can integrate seamlessly with external processors, enabling AI performance scaling up to three or four times the native 400 TOPS. This flexibility provides OEMs and Tier-1 suppliers with customizable options to meet evolving vehicle demands, offering scalability for future performance upgrades across diverse vehicle platforms.

Robust Security with Mixed-Criticality Processing

In the automotive industry, safety remains paramount. The R-Car X5H uses hardware-based Freedom from Interference (FFI) technology to securely isolate critical safety functions, such as brake-by-wire, from other non-critical operations. This mixed-criticality processing enables secure, independent domains for safety-critical tasks, preventing failures from impacting vital vehicle functions. Coupled with real-time Quality of Service (QoS) management, the SoC dynamically prioritizes processing tasks to ensure optimal performance under varied conditions.

The Path Forward for Software-Defined Vehicles

As part of Renesas’ R-Car Gen 5 family, the R-Car X5H is designed to address the requirements of the SDV market. By centralizing processing, this SoC streamlines vehicle electronic systems, supporting cross-domain applications like ADAS, IVI, and body control. Renesas’ new R-Car Open Access (RoX) platform provides a development environment with essential hardware, operating systems, and tools for seamless SDV development. This platform accelerates development and enables continuous software updates, critical in the SDV era.

A Vision for Automotive Innovation

The R-Car X5H’s impact on automotive technology is underscored by Asif Anwar, Executive Director of Automotive Market Analysis at TechInsights, who notes that the shift to SDVs will drive the market for high-performance compute SoCs. With its advanced 3-nm process, Renesas’ new SoC enables OEMs to meet power and performance demands across vehicle platforms, enhancing the integration of critical features within zonal and centralized controllers.

Renesas is showcasing the R-Car Gen 5 platform at electronica 2024 in Munich, where the development environment will be demonstrated. This advancement in automotive compute technology paves the way for a new generation of vehicles defined by powerful, efficient, and adaptable SoCs.

The post Renesas Launches Industry-First 3nm Multi-Domain SoC for Automotive, Revolutionizing Software-Defined Vehicle Development appeared first on ELE Times.

In the fast-evolving landscape of AI servers, telecom infrastructure, and battery management systems (BMS), reliable and efficient power management is critical. Infineon Technologies AG has unveiled the OptiMOS 5 Linear FET 2, a next-generation MOSFET engineered to address the complex demands of safe hot-swap operation and battery protection.

This advanced device bridges the gap between the low RDS(on) performance of trench MOSFETs and the robust safe operating area (SOA) of classic planar MOSFETs. By balancing these characteristics, the OptiMOS 5 Linear FET 2 ensures enhanced reliability in high-power applications.

Key Features and Benefits

1. Robust Safe Operating Area (SOA):
The OptiMOS 5 Linear FET 2 offers a 12x higher SOA at 54 V for 10 ms and a 3.5x improvement at 100 µs compared to standard OptiMOS 5 MOSFETs with similar RDS(on). These advancements are crucial for handling high inrush currents during hot-swapping in AI servers and telecom systems.

2. Low RDS(on):
The device minimizes operational losses, boosting energy efficiency. This is particularly significant in applications requiring long-term reliability, such as data centers and telecom infrastructures.

3. Improved Current Sharing for BMS Applications:
Optimized transfer characteristics enable precise current distribution among parallel MOSFETs, a critical factor in battery protection scenarios like short-circuit events. This ensures system reliability and simplifies design.

4. Reduced Component Count and Cost:
The enhanced SOA and current-sharing capabilities allow for up to a 60% reduction in components in designs driven by short-circuit current requirements. This reduction translates into lower bill-of-material (BOM) costs, improved design flexibility, and higher power density.

5. Versatile Packaging:
Available in a TO-leadless package (TOLL), the device supports a broader range of applications, offering designers the flexibility to create compact, high-density solutions.

Applications

The OptiMOS 5 Linear FET 2 is optimized for diverse applications requiring robust hot-swap and battery protection capabilities, including:
– AI servers and telecom systems, where safe hot-swapping ensures operational continuity.
– Battery management systems (BMS): Protects batteries from high inrush currents and short circuits, ensuring system longevity and safety.
– Battery-powered devices and tools, such as power tools, e-bikes, and e-scooters, where reliability and efficiency are paramount.
– Industrial applications, including forklifts and battery backup units, where energy efficiency drives operational savings.

Advancements Over Previous Generations

Compared to its predecessor, the OptiMOS Linear FET, the OptiMOS 5 Linear FET 2 delivers significant improvements in SOA at elevated temperatures, reduced gate leakage, and a wider range of packages. These enhancements enable more MOSFETs to be connected in parallel, increasing design flexibility and reducing overall system costs.

Supporting the Future of Power Electronics

Infineon’s OptiMOS 5 Linear FET 2 exemplifies the company’s commitment to providing cutting-edge solutions for power electronics. By enabling safe hot-swap operation and robust battery protection, this MOSFET addresses critical challenges in high-power applications. Its superior performance, cost efficiency, and versatility position it as a key enabler for future advancements in energy-efficient, high-reliability systems.

With its ability to meet the demanding requirements of modern applications, the OptiMOS 5 Linear FET 2 sets a new benchmark for power MOSFETs, paving the way for more efficient and reliable power systems.

The post OptiMOS Linear FET 2 MOSFET enables optimal hot-swap and battery protection appeared first on ELE Times.

Achieves approx. 1.3 times the creepage distance compared to standard products.

ROHM has developed surface mount SiC Schottky barrier diodes (SBDs) that improve insulation resistance by increasing the creepage distance between terminals. The initial lineup includes eight models – SCS2xxxNHR – for automotive applications such as onboard chargers (OBCs), with plans to  deploy eight models – SCS2xxxN – for industrial equipment such as FA devices and PV inverters in December 2024.

The rapidly expanding xEV market is driving the demand for power semiconductors, among them SiC SBDs, that provide low heat generation along with high-speed switching and high-voltage capabilities in applications such as onboard chargers. Additionally, manufacturers increasingly rely on compact surface mount devices (SMDs) compatible with automated assembly equipment to boost manufacturing efficiency. Compact SMDs tend to typically feature smaller creepage distances, fact that makes high-voltage tracking prevention a critical design challenge.

As leading SiC supplier, ROHM has been working to develop high-performance SiC SBDs that offer breakdown voltages suitable for high-voltage applications with ease of mounting. Adopting an optimized package shape, it achieves a minimum creepage distance of 5.1mm, improving insulation performance when contrasted with standard products.

The new products utilize an original design that removes the center pin previously located at the bottom of the package, extending the creepage distance to a minimum of 5.1mm, approx. 1.3 times greater than standard products. This minimizes the possibility of tracking (creepage discharge) between terminals, eliminating the need for insulation treatment through resin potting when surface mounting the device on circuit boards in high voltage applications.  Additionally, the devices can be mounted on the same land pattern as standard and conventional TO-263 package products, allowing an easy replacement on existing circuit boards.

Two voltage ratings are offered, 650V and 1200V, supporting 400V systems commonly used in xEVs as well as higher voltage systems expected to gain wider adoption in the future. The automotive-grade SCS2xxxNHR are AEC-Q101 qualified, ensuring they meet the high reliability standards this application sector demands.

Going forward, ROHM will continue to develop high-voltage SBDs using SiC, contributing to low energy consumption and high efficiency requirements in automotive and industrial equipment by providing optimal power devices that meet market needs.

Terminology

Creepage Distance

The shortest distance between two conductive elements (terminals) along the surface of the device package. In semiconductor design, insulation measures with such creepage and clearance distances must be taken to prevent electric shocks, leakage currents, and short-circuits in semiconductor products.

Tracking (Creepage Discharge)

A phenomenon where discharge occurs along the surface of the package (insulator) when high voltage is applied to the conductive terminals. This can create an unintended conductive path between patterns, potentially leading to dielectric breakdown of the device. Package miniaturization increases the risk of tracking by reducing creepage distance.

Resin Potting

The process of encapsulating the device body and the electrode connections between the device and circuit with resin, such as epoxy, to provide electrical insulation. This provides durability and weather resistance by protecting against water, dust, and other environmental conditions.

AEC-Q101 Automotive Reliability Standard

AEC stands for Automotive Electronics Council, a reliability standard for automotive electronic components established by major automotive manufacturers and US electronic component makers. Q101 is a standard that specifically applies to discrete semiconductor products (i.e. transistors, diodes).

The post ROHM’s New SiC Schottky Barrier Diodes for High Voltage xEV Systems: Featuring a Unique Package Design for Improved Insulation Resistance appeared first on ELE Times.

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TICP Series IsoVu current probes from Tektronix provide complete galvanic RF isolation between the measurement system and DUT. This isolation eliminates ground loops and significantly reduces common mode noise. The probes ensure high precision and safety when measuring fast-changing, shunt-based current in both low- and high-voltage systems.

The series includes three models with bandwidths of 1 GHz, 500 MHz, and 250 MHz. According to Tektronix, TICP current probes deliver over 30 times the common-mode rejection of conventional differential voltage probes, achieving 140 dB CMRR at DC and up to 90 dB at 1 MHz.

With minimal noise contribution, the 50-Ω probe input in a 1X configuration provides ultra-low noise levels of less than 4.7 nV/√Hz, or under 150 µV at 1 GHz. TICP probes use a TekVPI interface and work seamlessly with Tektronix 4, 5, and 6 series MSO oscilloscopes.

The TICP Series IsoVu probes are now available for order and will start shipping this month.

TICP Series product page

Tektronix

Find more datasheets on products like this one at Datasheets.com, searchable by category, part #, description, manufacturer, and more.

The post Current shunt probes feature RF isolation appeared first on EDN.

NXP’s i.MX 94 family of application processors combines communications, safety, and real-time control functions into a single SoC. They include a 2.5-Gbps Ethernet time-sensitive networking (TSN) switch, enabling configurable, secure communications with protocol support for both industrial and automotive applications. These processors are suited for industrial control, programmable logic controllers, telematics, industrial and automotive gateways, and building and energy control.

The 64-bit processors employ up to four Arm Cortex-A55 cores for Linux operation, complemented by two Cortex-M33 cores and two Cortex-M7 cores for enhanced real-time processing. This multicore architecture delivers low latency across both application and real-time domains. The devices also include an eIQ Neutron neural processing unit and a functional safety island to ensure compliance with IEC 61508 SIL2 and ISO 26262 ASIL-B standards.

To safeguard against quantum computing attacks, the i.MX 94 processors support post-quantum public key cryptography. An integrated EdgeLock secure enclave enables the system to configure and restore equipment to a trusted state at any time. It provides robust security features, including secure boot, secure debug, and secure updates, all leveraging post-quantum cryptography without compromising performance.

The i.MX 94 family of application processors is expected to begin sampling in Q1 2025.

i.MX 94 product page

NXP Semiconductors 

Find more datasheets on products like this one at Datasheets.com, searchable by category, part #, description, manufacturer, and more.

The post Application processors optimize industrial control appeared first on EDN.

IGBT Trench 7 devices from Microchip offer power system designers a wide selection of current and voltage ranges, topologies, and package types. Promising increased power capability, reduced power losses, and compact sizes, the IGBTs are key components in such applications as renewable energy systems, uninterruptible power supplies, commercial and agricultural vehicles, and More Electric Aircraft (MEA).

IGBT 7 modules support voltages from 1200 V to 1700 V and currents from 50 A to 900 A. Packaging options include standard D3 and D4 62-mm types, as well as SP6C, SP1F, and SP6LI. They are also available in various configurations and topologies, including three-level NPC, three-phase bridge, boost and buck choppers, dual-common source, full-bridge, phase leg, single switch, and T-type.

These power components provide lower on-state voltage, improved antiparallel diode performance, and higher current capacity, reducing power losses and enhancing efficiency. With low-inductance packages and high overload capability at a junction temperature of +175°C, they are useful for rugged aviation and defense applications. When used for motor control, they ensure smooth switching, improving reliability, reducing EMI, and minimizing voltage spikes.

IGBT Trench 7 devices are available now in production quantities.

IGBT Trench 7 product page

Microchip Technology 

Find more datasheets on products like this one at Datasheets.com, searchable by category, part #, description, manufacturer, and more.

The post Microchip launches broad IGBT 7 portfolio appeared first on EDN.

Select Tektronix FieldFox handheld analyzers now cover frequencies up to 170 GHz for mmWave signal analysis. In a collaboration with Virginia Diodes Inc. (VDI), Keysight’s A- and B-Series analyzers (18 GHz and up) can pair with VDI’s PSAX frequency extenders to reach sub-THz frequencies.

Precise mmWave measurements are essential for testing wireless communications and radar systems, particularly in 5G, 6G, aerospace, defense, and automotive radar applications. Because mmWave signals are sensitive to obstacles, weather, and interference, understanding their propagation characteristics helps engineers design more efficient networks and radar systems.

FieldFox with PSAX allows users to capture accurate mmWave measurements in a lightweight, portable package. It supports in-band signal analysis through selectable spectrum analyzer, IQ analyzer, and real-time spectrum analyzer modes, achieving typical sensitivity of -155 dBm/Hz.

The PSAX module connects directly to the RF ports on the FieldFox analyzer. Its adjustable IF connector aligns with the LO and IF port spacings on all FieldFox models. VDI also offers the PSGX module, which, when paired with a FieldFox equipped with Option 357, enables mmWave signal generation up to 170 GHz.

FieldFox product page

PSAX module product page

Keysight Technologies 

Find more datasheets on products like this one at Datasheets.com, searchable by category, part #, description, manufacturer, and more.

The post Portable test gear enables mmWave signal analysis appeared first on EDN.

Renesas has launched the AnalogPAK series of programmable mixed-signal ICs, including a 14-bit SAR ADC with a programmable gain amplifier. According to the company, this industry-first device combines a rich set of digital and analog features to support measurement, data processing, logic control, and data output.

AnalogPAK devices, a subset of the GreenPAK family, are NVM-programmable ICs that enable designers to integrate multiple system functions. ICs in both groups minimize component count, board space, and power and can replace standard mixed-signal products and discrete circuits. They also provide reliable hardware supervisory functions for SoCs and microcontrollers.

The SLG47011 multichannel SAR ADC offers user-defined power-saving modes for all macrocells. Designers can switch off some blocks in sleep mode to reduce power consumption to the microamp level. Key features include:

• VDD range of 1.71 V to 3.6 V
• SAR ADC: up to 14-bit, up to 2.35 Msps in 8-bit mode
• PGA: six amplifier configurations, rail-to-rail I/O, 1x to 64x gain
• DAC: 12-bit, 333 ksps
• Hardware math block for multiplication, addition, subtraction, and division
• 4096-word memory table block
• Oscillators: 2/10 kHz and 20/40 MHz
• Analog temperature sensor
• Configurable counter/delay blocks
• I2C and SPI communication interfaces
• Available in a 16-pin, 2.0×2.0×0.55-mm QFN package

In addition to the SLG47011, Renesas announced three other AnalogPAK devices. The compact SLG47001 and SLG47003 enable precise, cost-effective measurement systems for applications like gas sensors, power meters, servers, and wearables. The SLG47004-A is an automotive Grade 1 qualified device for infotainment, navigation, chassis and body electronics, and automotive display clusters.

The AnalogPAK devices are available now from Renesas and authorized distributors.

Renesas Electronics 

Find more datasheets on products like this one at Datasheets.com, searchable by category, part #, description, manufacturer, and more.

The post Renesas expands line of programmable mixed-signal ICs appeared first on EDN.

Toshiba Electronic Devices & Storage Corp has developed new 1200V silicon carbide (SiC) MOSFETs with an innovative structure that delivers both low on-resistance (RDS(ON)) and high reliability. The devices are particularly suited to automotive applications such as traction inverters. They are now available and shipping as early test samples in bare die format, allowing users to customize the bare die to meet their specific design needs and realize solutions for their applications...

In the early days of small (e.g., personal) computers, incorporation of one or two (or more) RS232 serial ports as general purpose I/O adaptors was common practice. Recently, this “vintage” standard has been largely replaced (after all, it is 64 years old) by faster and more power thrifty serial interface technologies (e.g., USB, I2C, SPI). Nevertheless, RS232 hardware is still widely and inexpensively available, and its bipolar signaling levels remain robustly noise and cable-length-effects resistant. Another useful feature is the bipolar supply voltages (usually +/-6 V) generated by typical RS232 adaptors. These can be conveniently tapped into via standard RS232 output signals (e.g., RTS and TXD) and used to power attached analog and digital circuitry.

Wow the engineering world with your unique design: Design Ideas Submission Guide

This design idea (DI) does exactly that by using asynchronous RS232 to power and count pulses from a simple 10 kHz voltage-to-frequency converter (VFC). Getting only one bit of info from each 10-bit serial character may seem inefficient (because it is), but in this case it’s a convenient ploy to add a simple analog input that can be located remotely from the computer with less fear of noise pickup.

See Figure 1 for the mind meld of RS232 with VFC.

Figure 1 A 10-kHz VFC works with and is powered by a generic RS232 port.

Much of the core of Figure 1 was previously described in “Voltage inverter design idea transmogrifies into a 1MHz VFC.”

One difference, other than the 100x lower max frequency, between that older DI and this one is the use of a metal gate CMOS device (CD4053B) for U1 instead of a silicon gate (HC4053) U1. That change is made necessary by the higher operating voltage (12 V versus 5 V) used here. Other design elements remain (roughly) similar.

Input current = Vin/R1, charges C3 which causes transconductance amplifier Q1,Q2 to sink, increasing current from Schmidt trigger oscillator cap C1. This increases U1c oscillator frequency and the current pumped by U1a,b and C2. Because the pump current has negative polarity, it completes a feedback loop that continuously balances pump current to equal input current:

Note that R1 can be chosen to implement almost any desired Vin full-scale factor.

D3 provides the ramp reset pulse that initiates each oscillator cycle and also sets the duration of the RS232 ST start pulse to ~10 µs as illustrated in Figure 2. Note that this combination of time constants and baud rate gives ~11% overrange headroom.

Figure 2 Each VFC pulse generates a properly formatted, but empty, RS232 character.

The ratio of R5/R3 is chosen to balance Q2/Q1 collector currents when Vin and Fpump equal zero, thus minimizing Vin zero offset. Consequently, linearity and zero offset errors are less than 1% of full-scale.

However, this leaves open the possibility of unacceptable scale factor error if the +6 logic power rail isn’t accurate enough, which it’s very unlikely to be. If we want a precision voltage reference that’s independent of +6 V instability, the inexpensive accurate 5 V provided by U2, C5, and R7 will fill the bill. 

However, if the application involves conversion of a ratiometric signal proportional to +6 V such as provided by a resistive sensor (e.g., thermistor), then U2 and friends should be omitted, U1 pin 2 connected to -6 V, and C2 reduced to 1.6 nF. Then:

 Stephen Woodward’s relationship with EDN’s DI column goes back quite a long way. Over 100 submissions have been accepted since his first contribution back in 1974.

 Related Content

• Voltage inverter design idea transmogrifies into a 1MHz VFC
• A simple, accurate, and efficient charge pump voltage inverter for $1 (in singles)
• Single supply 200kHz VFC with bipolar differential inputs
• New VFC uses flip-flops as high speed, precision analog switches
• Inexpensive VFC features good linearity and dynamic range
• Turn negative regulator “upside-down” to create bipolar supply from single source

The post RS232 meets VFC appeared first on EDN.

Introduction: Engineered systems

 

Human inventions, namely engineered systems, have relied on fundamental discoveries in physics and mathematics, e.g., Maxwell’s equations, Quantum mechanics, Information theory, etc., thereby applying these concepts to achieve a particular goal. However, engineered systems are rapidly growing in complexity and size, where the functionality of subcomponents may be nonlinear in nature and starting from these first principles is restrictive. MathWorks has steadily laid a foundation in modeling and simulation with MATLAB/Simulink for over four decades and now assists designers with these complex, multivariate systems with AI. 

Houman Zarrinkoub, MathWorks principal product manager for wireless communications, discussed with EDN the growing role AI plays in the design of next generation wireless systems. 

MATLAB’s toolboxes for wireless design

“So you’re building a wireless system and, at a basic level, you have a transmission back and forth between, for example, a base station and a cell phone,” said Houman, “this is known as a link.” 

To begin, Houman explains at a very basic level engineers are building the two subsystems (transmitter and receiver) that “talk” to each other with this link. There are the digital components that will sample, quantize, and encode the data and the RF components that will generate the RF signal, upconvert, downconvert, mix, amplify, filter, etc. MATLAB has an array of established toolboxes such as the 5G Toolbox, LTE Toolbox, Wi-Fi Toolbox, and Satellite communications Toolbox that already assist with the design, simulation, and verification of all types of wireless signals from 5G NR and LTE to DVB-S2/S2X/RCS2 and GPS waveforms. This is extended to subcomponents with the tools including (but not limited to) the RF Toolbox, Antenna Toolbox, and Phase Array System Toolbox. 

Now with AI, two main design approaches are used leveraging the Deep Learning Toolbox, Reinforcement Learning Toolbox, and Machine Learning Toolbox. 

AI workflow 

The workflow includes four basic steps that are further highlighted in Figure 1. 

1. Data generation
2. AI training
3. Integration, simulation, and testing
4. Deployment and implementation

These basic steps are necessary for implementing any deep learning model in an application, but how does it assist with RF and wireless design? 

Figure 1 MATLAB workflow for implementing AI in wireless system design. Source: MathWorks

 Data generation: Making a representative dataset 

It goes without saying that data generation is necessary in order to properly train the neural network. For wireless systems, data can either be obtained from a real system by capturing signals with an antenna or done synthetically on the computer. 

The robustness of this data is critical. “The keyword is making a representative dataset, if we’re designing for a wireless system that’s operating at 5 GHz we have data at 2.4 GHz, it’s useless.” In order to ensure the system is well-designed the data must be varied including signal performance in both normal operating conditions and more extreme conditions. “You usually don’t have data for outliers that are 2 or 3 standard deviations from the mean, but if you don’t have this data your system will fail when things shift out of the comfort zone,” explains Houman. 

Houman expands on this by saying it is best for designers to have the best of both worlds and use both a real world, hardware-generated dataset as well as the synthetic dataset to include some of those outliers. “With hardware, there are severe limitations where you don’t have time to create all that data. So, we have the Wireless Waveform Generator App that allows you to generate, verify, and analyze your synthetic data so that you can augment your dataset for training.” As shown in Figure 2, the app allows designers to select waveform types and introduce impairments for more real world signal scenarios.

Figure 2 Wireless Waveform Generator application allows users to generate wireless signals and introduce impairments. Source: MathWorks

Transfer learning: Signal discrimination 

Then, AI training is performed to either train a model that was built from scratch or, to  train an established model (e.g., AlexNet, GoogleNet) to optimize it for your particular task; this is known as transfer learning. As shown in Figure 3, pretrained networks can be reused in a particular wireless application by adding new layers that allow the model to be more fine-tuned towards the specific dataset. “You turn the wireless signal, and in a one-to-one manner, transform it into an image,” said Houman when discussing how this concept was used for wireless design.

Figure 3 Pretrained networks can be reused in a particular wireless application by adding new layers that allow the model to be more fine-tuned towards the specific dataset. Source: MathWorks

“Every wireless signal is IQ samples, we can transform them into an image by taking a spectrogram, which is a presentation of the signal in time and frequency,” said Houman, “we have applied this concept to wireless to discriminate between friend or foe, or between 5G and 4G signals.” Figure 4 shows the test of a trained system that used an established semantic segmentation network (e.g., ResNet-18, MobileNetv2, and ResNet-5). The test used over-the-air (OTA) signal captures with software-defined radio (SDR). Houman elaborated, “So you send a signal and you classify, and based on that classification, you have multiple binary decisions. For example, if it’s 4G, do this; if it’s 5G to this, if it’s none of the above, do this. So the system is optimized by the reliable classification of the type of signal the system is encountering.”

Figure 4 Labeled spectrogram outputted by a trained wireless system to discriminate between LTE and 5G signals. Source: MathWorks

Building deep learning models from scratch Supervised learning: Modulation classification with built CNN

Modulation classification can also be accomplished with the Deep Learning Toolbox where users generate synthetic, channel-impaired waveforms for a dataset. This dataset is used to train a convolutional neural network (CNN) and tested with hardware such as SDR with OTA signals (Figure 5). 

Figure 5 Output confusion matrix of a CNN trained to classify signals by modulation type with test data using SDR. Source: MathWorks

“With signal discrimination, you’re using more classical classification so you don’t need to do a lot of work developing those trained networks. However, since modulation and encoding is not found on the spectrogram, most people will then choose to develop their models from scratch,” said Houman, “in this approach, designers will use MATLAB with Python and implement classical building blocks such as rectifier linear unit (ReLU) to build out layers in their neural network.” He continues, “Ultimately a neural network is built on components, you either connect them in parallel or serially, and you have a network. Each network element has a gain and training will adjust the gain of each network element until you converge on the right answer.” He mentions that, while a less direct path is taken to obtain the modulation type, systems that combine these allow their wireless systems to have a much deeper understanding of the signals they are encountering and make much more informed decisions.

Beam selection and DPD with NN

Using the same principles neural networks (NNs) can be customized within the MATLAB environment to solve inherently nonlinear problems such as applying digital predistortion (DPD) to offset the nonlinearities in power amplifiers (PAs). “DPD is a classical example of a nonlinear problem. In wireless communications, you send a signal, and the signal deteriorates in strength as it leaves the source. Now, you have to amplify the signal so that it can be received but no amplifier is linear, or has constant gain across its bandwidth.” DPD attempts to deal with the inevitable signal distortions that occur when using a PA that is operating within its compression region by observing the output signal from the PA and using that as feedback for the alterations to the input signal so that the PA output is closer to ideal. “So the problem is inherently non-linear and many solutions have been proposed but AI comes along, and produces superior performance than other solutions for this amplification process,” said Houman. The MATLAB approach trains a fully connected NN as the inverse of the PA and uses it for DPD (NN-DPD), then, the NN-DPD is tested using a real PA and compared with a cross-term memory polynomial DPD. 

Houman goes on to describe another application for NN-based wireless design (Figure 6), “Deep learning also has a lot of applications in 5G and 6G where it combines sensing and communications. We have a lot of deep learning examples where different algorithms are used to position and localize users so you can send data that is dedicated to the user.” The use case that was mentioned in particular related to integrated sensing and communication (ISAC), “When I was young and programming 2G and 3G systems, the philosophy of communication was that I would send the signal in all directions, and if your receiver got that information, good for it; it can now decode the transmission. If the receiver couldn’t do that, tough luck,” said Houman, “With 5G and especially 6G, the expectations have risen, you have to have knowledge of where your users are and beamform towards them. If your beamwidth is too big, you lose energy. But, if your beamwidth is too narrow, if your users move their head, you miss them. So you have to constantly adapt.” In this solution, instead of using GPS signals, lidar, or roadside camera images, the base station essentially becomes the GPS locator; sending signals to locate users and based upon the returned signal, sends communications. 

Figure 6 The training phase and testing phase of a beam management solution that uses the 3D coordinates of the receiver. Source: MathWorks

Unsupervised learning: The autoencoder path for FEC 

Alternatively, engineers can follow the autoencoder path to help build a system from the ground up. These deep learning networks consist of an encoder and a decoder and are trained to replicate their input data to, for instance, remove noise and detect anomalies in signal data. The benefit of this approach is that it is unsupervised and does not require labeled input data for training. 

“One of the major aspects of 5G and 6G is forward error correction (FEC) where, when I send something to you, whether its voice or video, whether or not the channel is clean or noisy, the receiver should be able to handle it,” said Houman. FEC is a technique that adds redundant data to a message to minimize the number of errors in the received information for a given channel (Figure 7). “With the wireless autoencoder, you can automatically add redundancy  and redo modulation and channel coding based on estimations of the channel condition, all unsupervised.”

Figure 7 A wireless autoencoder system ultimately restricts the encoded symbols to an effective coding rate for the channel. Source: MathWorks

Reinforcement learning: Cybersecurity and cognitive radar 

“With deep learning and machine learning, where the process of giving inputs and receiving an output will all be performed offline,” explained Houman. “With deep learning, you’ve come up with a solution and you simply apply that solution in a real system.” He goes on to explain how reinforcement learning must be applied to a real system at the start. “Give me the data and I will update that brain constantly.” 

Customers in the defense industry will leverage Reinforcement Learning Toolbox to, for example, assess all the vulnerabilities of their 5G systems and update their cybersecurity accordingly. “Based upon the vulnerability, they will devise techniques to overcome the accessibility of the unfriendly agent to the system.” Other applications might include cognitive radar where cognitive spectrum management (CSM) would use reinforcement learning to analyze patterns in the spectrum in real-time and predict future spectrum usage based upon previous and real-time data. 

Integration, simulation, and testing

As shown in many of these examples, the key to the third step in the workflow is to create a unique dataset to test the effectiveness of the wireless system. “If you use the same dataset to train and test, you’re cheating! Of course it will match. You have to take a set that’s never been seen during training but is still viable and representative and use that for testing,” explains Houman, “That way, there is a confidence that different environments can be handled by the system with the training we did in the first step of data gathering and training.” The Wireless Waveform Generator App is meant to assist with both these stages. 

Deployment and implementation

The MathWorks approach to deployment works with engineers at the language level with a more vendor-agnostic approach to hardware. “We have a lot of products that turn popular languages such into MATLAB code, to train and test the algorithm, and then turn that back into the code that will go into the hardware. For FPGAs and ASICs, for example, the language is Verilog or VHDL. We have a tool called the HDL Coder that will take the MATLAB and Simulink model and turn that into low level VHDL code to go into any hardware.” 

Addressing the downsides of AI with the digital twin

The natural conclusion of the interview was understanding the “catch” of using AI to improve wireless systems. “AI takes the input, trains the model, and produces an output. In that process, it merges all the system components into one. All those gains, they change together, so it becomes an opaque system and you lose insight into how the system is working,” said Houman. While this process has considerable benefit, troubleshooting issues can be much more challenging than debugging with solutions that leverage the traditional, iterative approach where isolating problems might be simpler. “So, in MathWorks, we are working on creating a digital twin of every engineered system, be it a car, an airplane, a spacecraft, or a base station.” Houman describes this as striking a balance between the  traditional engineered system approach and an AI-based engineering solution, “Any engineer can compare their design to the all-encompassing digital twin and quickly identify where their problem is. That way, we have the optimization of AI, plus the explainability of model-based systems. You build a system completely in your computer before one molecule goes into the real world.”

Aalyia Shaukat, associate editor at EDN, has worked in the engineering publishing industry for over 8 years. She holds a Bachelor’s degree in electrical engineering from Rochester Institute of Technology, and has published works in major EE journals as well as trade publications.

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The post Applying AI to RF design appeared first on EDN.

A new systems design toolset aims to create a unified user experience for board designers while adding cloud connectivity and artificial intelligence (AI) capabilities, which will enable engineers to adapt to rapidly changing design and manufacturing environments.

To create this highly integrated and multidisciplinary tool, Siemens EDA has combined its Xpedition system design software with Hyperlynx verification software and PADS Professional software for integrated PCB design. The solution also includes Siemens’ Teamcenter software for product lifecycle management and NX software for product engineering.

Figure 1 The new systems design toolset enhances integration by combining multiple design tools. Source: Siemens EDA

Evolution of systems design

Systems design—spanning from IC design and manufacturing to IC packaging and board design to embedded software—has been constantly evolving over the past decades, and so have toolsets that serve these vital tenets of electronics.

Take IC design, for instance, which is now carried out by multiple outfits. Then, there are PCBs that unified early on with large vendors offering front-to-back solutions. PCBs then entered an era of multidiscipline design, needing more design automation. Finally, we entered the modern era that encompasses cloud computing and AI.

Siemens EDA’s next-generation electronic systems design software takes an integrated and multidisciplinary approach to cater to this changing landscape. David Wiens, project manager for Xpedition at Siemens EDA, told EDN that this solution took five years to develop through extensive beta cycles with designers to validate generational shifts in technologies. It’s built on five pillars: Intuition, AI, cloud, integration, and security.

Figure 2 The next-generation electronic system design aims to deliver an intuitive, AI-enhanced, cloud-connected, integrated, and secure solution to empower engineers and organizations in today’s dynamic environment. Source: Siemens EDA

But before explaining these fundamental tenets of electronic systems design, he told EDN what drove this initiative in the first place.

1. Workforce in transition

A lot of engineers are retiring, and their expertise is going with them, creating a large gap for young engineers. Then there is this notion that companies haven’t been hiring for a decade or so and that there is a shortage of new engineers. “The highly intuitive tools in this systems design solution aim to overcome talent shortages and enable engineers to quickly adapt with minimal learning curves,” Wiens said.

2. Mass electrification

Mass electrification leads to a higher number of design starts, faster design cycles, and increased product complexity. “This new toolset adds predictive engineering and new support assistance using AI to streamline and optimize the design workflows,” said Wiens.

3. Geopolitical and supply chain volatility

Wiens said that the COVID era introduced some supply chain challenges while some challenges existed before that due to geopolitical tensions. “COVID just magnified them.”

The new electronic systems design solution aims to address these challenges head-on by providing a seamless flow of data and information throughout the product lifecycle using digital threads. It facilitates a unified user experience that combines cloud connectivity and AI capabilities to drive innovation in electronic systems design.

Below is a closer look at the key building blocks of this unified solution and how it can help engineers to tackle challenges head-on.

1. Intuitive

The new toolset boosts productivity with a modern user experience; design engineers can start with a simple user interface and switch to a complex user interface later. “We have taken technologies from multiple acquisitions and heritages,” said Wiens. “Each of those had a unique user experience, which made it difficult for engineers to move from one environment to the next.” So, Siemens unified that under a common platform, which allows engineers to seamlessly move from tool to another.

Figure 3 The new toolset allows engineers to seamlessly move from one tool to another without rework. Source: Siemens EDA

2. AI infusion

AI infusion accelerates design optimization and automation. For instance, with predictive AI, design engineers can leverage simulation engines from a broader Siemens portfolio. “The goal is to expand engineering resources without necessarily expanding the human capital and compute power,” Wiens said.

Figure 4 The infusion of AI improves design process efficiency and leverages the knowledge of experienced engineers in the systems design environment. Source: Siemens EDA

Here, features like chat assistance systems allow engineers to ask natural language questions. “We have a natural language datasheet query, which returns the results in natural language, making it much simpler to research components,” he added.

3. Cloud connected

While cloud-connected tools enable engineers to collaborate seamlessly across the ecosystem, PCB tools are practically desktop-based. In small- to mid-sized enterprises, some engineers are shifting to cloud-based tools, but large enterprises don’t want to move to cloud due to perceived lack of security and performance.

Figure 5 Cloud connectivity facilitates collaboration across the value chain and provides access to specialized services and resources. Source: Siemens EDA

“Our desktop tools are primary offerings in a simulation environment, but we can perform managed cloud deployment for design engineers,” said Wiens. “When designers are collaborating with outside engineering teams, they often struggle collaborating with partners. We offer a common viewing environment residing in the cloud.”

4. Integration

Integration helps break down silos between different teams and tools in systems design. Otherwise, design engineers must spend a lot of time in rework to create the full model when moving from one design tool to another. The same thing happens between design and manufacturing cycles; engineers must rebuild the model in the manufacturing phase.

The new systems design toolset leverages digital threads across multiple domains. “We have enhanced integration with this release to optimize the flow between tools so engineers can control the ins and outs of data,” Wiens said.

5. Security

Siemens, which maintains partnerships with leading cloud providers to ensure robust security measures, manages access control based on user role, permission, and location in this systems design toolset. The next-generation systems design offers rigid data access restrictions that can be configured and geo-located.

“It provides engineers with visibility on how data is managed at any stage in design,” said Wiens. “It also ensures the protection of critical design IP.” More importantly, security aspects like monitoring and reporting behavior and anomalies lower the entry barriers for tools being placed in cloud environments.

Need for highly integrated toolsets

The electronics design landscape is constantly changing, and complexity is on the rise. This calls for more integrated solutions that make collaboration between engineering teams easier and safer. These new toolsets must also take advantage of new technologies like AI and cloud computing.

With the evolution of the electronics design landscape, that’s how toolsets can adapt to changing realities such as organization flexibility and time to productivity.

Related Content

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• ‘Cloud Computing Is Changing Everything About Electronic Design’
• How highly skilled engineers manage complexity of embedded systems design

The post Shift in electronic systems design reshaping EDA tools integration appeared first on EDN.

Akash Systems of Oakland, CA, USA has signed a non-binding preliminary memorandum of terms with the US Department of Commerce under the CHIPS and Science Act to receive over $68m in direct funding. This includes $18.2m in proposed direct funding and $50m in combined federal and California state tax credits. This proposed funding would provide Akash, a pioneer of diamond cooling semiconductor technologies, significant support for its operational ramp-up in AI, data centers, space and defense markets...

Fabless firm Cambridge GaN Devices Ltd (CGD) — which was spun out of the University of Cambridge in 2016 to design, develop and commercialize power transistors and ICs that use GaN-on-silicon substrates — and IFP Energies nouvelles (IFPEN) — a French public research and training organization in the fields of energy, transport and the environment — have developed a demo that confirms the suitability of CGD’s ICeGaN650V GaN ICs in a multi-level, 800VDC inverter...

BluGlass Ltd of Silverwater, Australia — which develops and manufactures gallium nitride (GaN) blue laser diodes based on its proprietary low-temperature, low-hydrogen remote-plasma chemical vapor deposition (RPCVD) technology — has entered into a US$1.925m (AUS$2.9m) contract with North Carolina State University (NCSU) for visible laser development activity as part of the Commercial Leap Ahead for Wide Bandgap Semiconductors (CLAWS) Hub...

Gallium nitride (GaN) power IC and silicon carbide (SiC) technology firm Navitas Semiconductor Corp of Torrance, CA, USA and Richardson Electronics Ltd of LaFox, IL, USA have announced an expanded distribution partnership for silicon carbide (SiC) power semiconductors for Europe, the Middle East and Africa (EMEA)...

Anritsu Corporation has joined the OpenROADM Multi-Source Agreement (MSA), which defines specifications that facilitate multi-vendor interoperability for optical transmission networks. Anritsu’s participation in these efforts aims to enhance efficiency and flexibility of the optical transmission network. As Test & Measurement instrument vendor, Anritsu’s OpenROADM activities contribute to the openness and efficiency of optical transmission networks by promoting interconnect specifications and interoperability verification.

“Anritsu is thrilled to become the inaugural Test & Measurement instrument vendor in the OpenROADM MSA, comprising service providers and vendors. The OpenROADM MSA Group is committed to the open evolution of network management and vendor interoperability. We eagerly anticipate collaborating on network orchestration, which will enable us to control and monitor the quality of the entire network. Additionally, we are excited about the prospect of introducing test & measurement innovations facilitating network fault detection and causal analysis.” says Tadanori Nishikobara, Marketing Director of the Service Infrastructure Solutions Division at Anritsu Corporation.

Anritsu contributes to OpenROADM activities through test & measurement proposals and support for interoperability verification.

The post Anritsu joins OpenROADM as Test & Measurement instrument vendor appeared first on ELE Times.

• Collaboration enables FieldFox handheld analyzers to support up to 170 GHz, offering a rugged and lightweight portable solution for millimeter-wave signal analysis
• Provides in field-testing capabilities for aerospace and defense applications reducing development time

Keysight Technologies, Inc. has expanded the frequency range of its FieldFox handheld signal analyzers, offering up to 170 GHz support for millimeter-wave (mmWave) signal analysis. Through a collaboration with Virginia Diodes Inc. (VDI), Keysight’s A- and B-Series FieldFox handheld analyzers with 18 GHz or higher, can be paired with VDI PSAX frequency extenders to cover sub-THz frequency range.

Field based engineers need precise mmWave measurements to advance modern wireless communications and radar systems. This is critical when it comes to 5G, 6G, aerospace and defense and automotive radar transmission/receiving tests. However, mmWave signals are highly sensitive to obstacles, weather conditions, and interference. Understanding their propagation characteristics through precise measurements helps engineers design more efficient networks and radar systems, improving coverage, and enhancing reliability.

To gain this insight, traditional tools typically include large desktop signal analyzers and generators, which are often very expensive and cumbersome for field measurement use cases. The Keysight FieldFox addresses this issue, enabling mmWave measurements in a lightweight portable solution, when paired with VDI’s PSAX frequency extender modules. In addition, engineers can opt for the FieldFox equipped with the downloadable Option 357 pulse generator, which can be paired with a PSGX module from VDI, to also offer a mmWave signal generation solution up to 170 GHz. This enables users to obtain accurate mmWave measurements in a simple, easy to use and rugged solution.

Key benefits of Keysight’s FieldFox combined with VDI frequency extender modules include:

• Expanded frequency coverage: Expanding the FieldFox’s frequency coverage from as low as 18 GHz, depending on models, up to 170 GHz for either signal analysis or generation.
• Optimized performance at the mmWave range: Supporting in-band signal analysis with selection of spectrum analyzer mode, IQ analyzer mode, or real-time spectrum analyzer (RTSA) mode with extraordinary sensitivity of -155 dBm/Hz typical value.
• Cost efficiency: Compared to traditional mmWave signal analysis and generation solutions, the combination of Keysight FieldFox and VDI frequency extenders reduces costs by half or more.
• Portable and convenient testing: Weighing approximately less than 4 kg in total, the combination of Keysight FieldFox and VDI frequency extenders makes the mmWave field testing much more feasible and convenient for both field and lab environment.

Dr. Thomas W. Crowe, CEO of VDI, said: “VDI manufactures state-of-the-art test and measurement equipment for mmWave and THz applications, including vector network analyzer, spectrum analyzer, and signal generator extension modules. These products enhance the capabilities of high-performance microwave measurement tools by extending them to higher frequencies. Through our collaboration with Keysight, VDI is excited to provide frequency extenders for the FieldFox handheld analyzers, offering customers lightweight solutions for both signal analysis and generation in the mmWave range, with exceptional signal quality and measurement integrity.”

Vince Nguyen, Vice President and General Manager, Aerospace, Defense, and Government Solution Group at Keysight, said: “The aerospace, defense, and commercial sectors lack a portable solution which can provide accurate mmWave measurements. As customers explore innovations they need access to higher frequencies in the radio spectrum, including mmWave. Working with VDI, we’ve developed a solution that is easy to test signal analysis and generation in the field as well as in the laboratory.”

Keysight’s FieldFox combined with VDI extenders will be showcased for the first time at the Keysight booth (Hall A3, Stand 506) at electronica 2024, the world’s leading trade fair and conference for electronics, from November 12 to 15, 2024.

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