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ABB and Formula E partner to write the future of e-mobility



“ABB FIA Formula E Championship” brings pioneering technology leader ABB as title sponsor to the world’s first fully electric international FIA motorsport series.

ABB and Formula E are teaming up in a ground-breaking partnership to champion e-mobility for a sustainable future. Since its first race in Beijing in September 2014, Formula E has established itself as the number one all-electric international motor sport. In the next level of development, global pioneering technology leader ABB is bringing its name and innovation and technology leadership to the series, which will be now known as the “ABB FIA Formula E Championship.”

With its unrivalled expertise in electrification and leadership in electric vehicle charging solutions, with the largest installed base of fast-charging stations for electric vehicles worldwide, ABB is the ideal industry partner for Formula E. Formula E serves as a competitive platform to develop and test e-mobility-relevant electrification and digitalization technologies, helping refine the design and functionality of electric vehicles and infrastructure as well as the associated digital platforms. By joining forces, ABB and Formula E will be ideally positioned to push the boundaries of e-mobility.

“We are extremely excited to partner with Formula E in writing the future of e-mobility,” said ABB CEO Ulrich Spiesshofer. “Today, two pioneers are uniting. ABB and Formula E are a natural fit at the forefront of the latest electrification and digital technologies. Together, we will write the next phase of this exciting sports activity and foster high-performance teams. Together, we will write the future – one electrifying race at a time.”

Alejandro Agag, founder and CEO of Formula E, said: “This is a historic day for Formula E and I’m honored to welcome the global technology leader ABB as the title partner of Formula E, with its background and expertise in the field of electrification and digital technologies. Our two companies are synonymous with pushing the boundaries of what is possible. Together, as partners, we will showcase breakthrough technology on a global scale to fans and consumers who follow the ABB FIA Formula E Championship.”

As the world leader in electric vehicle infrastructure, ABB offers the full range of charging solutions for electric cars, electric and hybrid buses as well as electrification solutions for ships and railways. ABB entered the EV-charging market back in 2010, and today has a fast growing global installed base of more than 6,000 fast chargers.

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Industrial Hardware and Machine Parts

MICROCHIP non-volatile memories



The demand for non-volatile memory is largely due to the continuous development of mobile devices, which require more and more memory capacity. This particularly applies to cameras, smartphones, tablets or cameras. Growing market expectations propel the ongoing improvement of non-volatile memory manufacturing technologies.

The core idea behind non-volatile memory is to store data when there is no power supply. However, the power supply is necessary for data saving and reading.

Both Microchip and Atmel (which was acquired by Microchip) have extensive experience in the manufacture of non-volatile memories. The manufacturing process is carried out in the company’s own silicon factories. Advanced test procedures are employed to maintain the highest level of quality. The manufacturer’s portfolio also includes AEC-Q100 certified memories for automotive applications. It is also worth mentioning that all memory chips introduced so far on the market are still manufactured.


EEPROM (Electrically Erasable Programmable Read Only Memory) memories belong to the group of non-volatile memories. Solutions of this type are most often used in applications which require the presence of reprogrammable areas of ROM memory, especially with regard to storing system configuration data.

Taking into account the interface, EEPROMs can be serial or parallel. Serial memories (24xx series with I2C interface, 25xx series with SPI interface, 93xx series with Microwire interface) are usually manufactured in DIP and SOIC enclosures. Their capacity usually amounts to several dozen kB. It is thanks to the serial interface, small size and low energy consumption that such memories are very often used to store device serial number data or configuration and manufacturing data. There are also serial memories with a unique 48- or 64-bit address that is pre-programmed in the factory, which can be used as the MAC address of the device.

The 28xx series includes parallel memories. It is worth mentioning that they are compatible with EPROM 27xxx series memories in terms of reading and output features.

The area of application of EEPROM memories mainly includes industrial electronics – measuring devices and control systems, safety and alarm systems, sensors or battery chargers. They can also be found in IoT devices, medical devices and in the automotive segment. Moreover, EEPROMs are also applied in consumer electronics, i.e. in computer equipment, household appliances & audio/video devices.

The fact that Microchip continues to support legacy EEPROMs – 1.2um – 0.7 – 0.5 – 0.4 – 0.25 – 0.18 – 0.13um – plays an important role in ensuring the continuity of device production.

The development of EEPROMs primarily involves reducing energy consumption and introducing support for new interfaces. The asynchronous UNI/O bus developed by Microchip in 2008 is worth mentioning here (11xx series). It is based on a single bi-directional SCIO (Single Connection I/O) data line, which gives a total of 3 outputs for SOT23 and TO92 enclosures. The latest solution is the memory with a Single-Wire interface (21CS series), in which the power is supplied to the system through a bi-directional data line, which allows to reduce the number of outputs from the system to just two (SI/O + GND). 


When compared to EEPROM, FLASH non-volatile memories are characterized by shorter write and read times, which, however, means that it is impossible to write and read single bytes. In this case, read and write are executed in larger areas of memory, so called pages (128/256 bytes). Flash memories offered by Microchip are fitted with a parallel (SST39 series) or serial interface (SPI in SST25 series, SQI in SST26 series). Key parameters of Flash memory include: memory capacity (4 Mbit), operating frequency (e.g. 40 MHz), operating voltage (e.g. 2.3 – 3.6V), housing type (e.g. TDFN8), mounting method (e.g. SMD) and operating temperature (e.g. -40-85°C). 

It is worth mentioning the SuperFlash technology used in the systems, which ensures reduced power consumption with a very short data deletion time. The SQI interface, on the other hand, provides fast data transmission with a minimum number of outputs.


EERAM is a combination of high-speed SRAM (Static Random-Access Memory) and non-volatile EEPROM, which stores a copy of SRAM (I2C, 47x series). Thanks to this configuration the contents of the cache can be restored from the backup copy in case of power supply problems. Therefore, EERAM is based on an external capacitor, which is the source of backup power for the time needed to copy the memory content.

It is worth mentioning similar NVSRAM (Non-volatile Static Random-Access Memory – 23XX series) systems, which also feature RAM backup. The difference is that for such chips to operate properly, an additional power supply is required, namely a battery or a rechargeable battery, (not needed in the case of EERAM), which has an impact on the device manufacturing cost.

Furthermore, the number of data writing and reading operations is unlimited. Depending on application requirements, you can choose an EERAM of 4kb or 16kb.

During operation, the internal logic is responsible for real-time power status monitoring. As a result, all power supply losses and drops are detected, taking into account the accepted threshold (Vtrip). If any of these statuses is detected, the SRAM content is copied to the EEPROM. The external capacitor connected to the Vcap output of the system is also important here. When the supply voltage returns above the Vtrip level, the EEPROM content is copied to the SRAM. It should be noted that the SRAM content can be restored at any time by means of a software trigger. To sum up, EERAMs are perfectly suited for use in applications where frequent and quick updating of memory cell content is required, while ensuring that the data stored there is preserved in case of power loss. They are therefore a perfect match for measuring instruments (electric, gas and liquid meters), industrial and consumer electronics (POS terminals, information kiosks, printers) and automotive solutions (data loggers, sensors).

More information is available on the website of Transfer Multisort Elektronik ( – an official distributor of Microchip Technology.

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Industrial Hardware and Machine Parts

Hexagon presents complete solution for laser scanning on the machine tool



New on-machine tool laser scanning measurement solution enhances productivity and data capture.

Hexagon’s Manufacturing Intelligence division is bringing laser scanning with metrology levels of precision to machine tool measurement with its new LS-C-5.8 system.

Ideal for measuring freeform or large surfaces, the LS-C-5.8 integrates with machine tools to create point cloud images of a part’s entire surface. Dedicated software presents the data in an easy-to-understand format, making it simple to quickly identify fluctuations in quality and correctly align a part for reworking while it is still clamped to the machine tool.

Andreas Hieble, Product Manager Metrology Solutions for Hexagon’s machine tool measurement product line says: “We’ve drawn on our expertise in developing market-leading laser scanners for coordinate measuring machines and portable measuring arms to meet manufacturers’ growing demand for a new, productivity-enhancing approach to machine tool measurement. Today, users typically have to create and analyse many single points when measuring with a machine tool. The LS-C-5.8 laser scanner solution transforms the process by automatically capturing thousands of points per second and rapidly delivering rich data in an easy-to-read form.” 

The LS-C-5.8 is a fixed blue line sensor that delivers precise results whether measuring shiny or very dark surfaces across a huge variety of applications and surface types. It combines a compact design with a large field-of-view so that it can be used to create point clouds on small machines and in environments where part accessibility is limited. And its software enables the comparison of the real-life part with designs in the CAD model.

The LS-C-5.8’s software is compatible with controls from Siemens, Fanuc and Heidenhain. It is designed to marry high performance with ease-of-use and its features include the display of colour-mapped point clouds. It is able to use data to align the part on the machine (Best-Fit) and can export files in an STL format. As a result, the data it captures can be ready in real time on the shop floor, enabling manufacturers to quickly identify and address production issues.

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Industrial Hardware and Machine Parts

AI Devices In Industrial Manufacturing To Reach 15.4mln By 2024



In recent years, Artificial Intelligence (AI) has been touted as a powerful technology that will revolutionise the industrial manufacturing space. The sentiment has its validity, but the reality is extremely complex.

AI in industrial manufacturing is a collection of various use cases at different phases of manufacturing, such as generative design in product development, production forecasting in inventory management, and machine vision, defect inspection, production optimisation, and predictive maintenance in the production phase. ABI Research, a global tech market advisory firm, forecasts that the total installed base of AI-enabled devices in industrial manufacturing will reach 15.4 million in 2024, with a CAGR of 64.8% from 2019 to 2024.

“AI in industrial manufacturing is a story of edge implementation,” says Lian Jye Su, Principal Analyst at ABI Research. “Since manufacturers are not comfortable having their data transferred to a public cloud, nearly all industrial AI training and inference workloads happen at the edge, namely on device, gateways and on-premise servers.” To facilitate this, AI chipset manufacturers and server vendors have designed AI-enabled servers specifically for industrial manufacturing. More and more industrial infrastructure is equipped with AI software or dedicated AI chipsets to perform AI inference.

Despite these solutions and the wealth of data in the manufacturing environment, the implementation of AI in industrial manufacturing has not been as seamless as was expected by the industry. Among all the use cases, predictive maintenance and equipment monitoring have been the most commercially implemented so far, due to the maturity of associated AI models. The total installed base for these two use cases alone is expected to reach 9.8 million and 6.7 million, respectively, by 2024. It is important to note that many of these AI-enabled industrial devices support multiple use cases on the same device due to advancements in AI chipsets. Key startups such as Uptake, SparkCognition, FogHorn and Falkonry are introducing cloud- and edge-based solutions that monitor the overall performance of industrial manufacturing assets and process flows.

Another commercial use case currently gaining momentum is defect inspection. The total installed base for this use case is expected to grow from 300,000 in 2019 to over 3.7 million by 2024. This is a use case that is extremely popular in electronic and semiconductor manufacturing, where major manufacturers, such as Samsung, LG and Foxconn, have been partnering with AI chipset vendors and software providers, such as CEVA, Gyrfalcon Technology, Lattice Semiconductor, Instrumental, Landing AI, and Neurala, to develop AI-based machine vision to perform surface, leak and component-level defect detection, microparticle detection, geometric measurement, and classification.

Conventional machine vision technology remains popular in the manufacturing factory, due to its proven repeatability, reliability, and stability. However, the emergence of deep learning technologies opens the possibility of expanded capabilities and flexibility. These algorithms can pick up unexpected product abnormalities or defects, go beyond existing issues and uncover valuable new insights for manufacturers.

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