Feed aggregator

Specialty analog foundry Tower Semiconductor Ltd of Migdal Haemek, Israel has announced the expansion of its existing, mature 300mm wafer bonding technology (originally developed and in mass production for stacked BSI image sensors) to enable heterogeneous 3D-IC integration across its silicon photonics (SiPho) and silicon germanium (SiGe) BiCMOS processes, including full support by Cadence design tools for the stacked platform technology...

Ascent Solar Technologies Inc of Thornton, CO, USA – which designs and makes lightweight, flexible copper indium gallium diselenide (CIGS) thin-film photovoltaic (PV) panels that can be integrated into consumer products, off-grid applications and aerospace applications – has signed a teaming agreement with Louisiana- and Texas-based NovaSpark Energy, which designs and manufactures mobile hydrogen generation systems for fueling long-range drones and powering critical infrastructure...

NUBURU Inc of Centennial, CO, USA — which was founded in 2015 and developed and previously manufactured high-power industrial blue lasers — has updated its strategic alliance with Tekne S.p.A. (a provider of integrated electronic warfare and cyber capabilities in military vehicles). The companies, along with Tekne’s shareholders, signed a new letter of agreement on 10 November, superseding their previous understanding and establishing a renewed strategic and industrial partnership. This agreement will be implemented by NUBURU via its specialized subsidiary Nuburu Defense LLC, which is focused on delivering laser-based solutions for defense, security and critical infrastructure applications...

submitted by /u/Whyjustwhydothat [link] [comments]

▶️ 43-я Міжнародна конференція IEEE з електроніки та нанотехнологій (ELNANO) kpi чт, 11/13/2025 - 10:00

Caliber Interconnects Pvt. Ltd., announced that it has achieved accelerated turnaround times and first-time-right outcomes for complex chiplet and Automated Test Equipment (ATE) hardware projects. The company has refined its proprietary design and verification workflow, which integrates powerful Cadence solutions to optimize performance, power, and reliability from the earliest stages of design.

Caliber’s advanced methodology significantly enhances the efficiency and precision of designing high-complexity IC packages and dense PCB layouts. By leveraging the Cadence Allegro X Design Platform for PCB and advanced package designs, which features sub- rawing management and auto- routing, Caliber’s teams can work in parallel across various circuit blocks, compressing overall project timelines by up to 80 percent. This streamlined framework is reinforced by a rigorous in-house verification process and custom automation utilities developed using the Allegro X Design Platform’s SKILL-based scripting, ensuring consistent quality and compliance with design rules.

To meet the demands of next-generation interconnects operating at over 100 Gbps, Caliber’s engineers utilize Cadence’s Sigrity X PowerSI and Sigrity X PowerDC solutions. These advanced simulation tools allow the team to analyze critical factors such as signal loss, crosstalk, and power delivery network (PDN) impedance. By thoroughly evaluating IR drop, current density, and Joule heating, Caliber can confidently deliver design signoff, reducing the risk of costly respins and speeding time to market for its customers.

“Our team has elevated our engineering leadership by creating a disciplined workflow that delivers exceptional quality and faster turnaround times for our customers across the semiconductor ecosystem,” said Suresh Babu, CEO of Caliber Interconnects. “Integrating Cadence’s advanced design and simulation environment into our proprietary methodology empowers us to push the boundaries of performance and reliability in complex chiplet and ATE hardware design.”

The post Caliber Interconnects Accelerates Complex Chiplet and ATE Hardware Design with Cadence Allegro X and Sigrity X Solutions appeared first on ELE Times.

The TCP/IP functionality of a connected device uses dynamic RAM allocation because of the unpredictable nature of network behavior. For example, if a device serves a web dashboard, we cannot control how many clients might connect at the same time. Likewise, if a device communicates with a cloud server, we may not know in advance how large the exchanged messages will be.

Therefore, limiting the amount of RAM used by the TCP/IP stack improves the device’s security and reliability, ensuring it remains responsive and does not crash due to insufficient memory.

Microcontroller RAM overview

It’s common that on microcontrollers, available memory resides in several non-contiguous regions. Each of these regions can have different cache characteristics, performance levels, or power properties, and certain peripheral controllers may only support DMA operations to specific memory areas.

Let’s take the STM32H723ZG microcontroller as an example. Its datasheet, in section 3.3.2, defines embedded SRAM regions:

Here is an example linker script snippet for this microcontroller generated by the CubeMX:

Ethernet DMA memory

We can clearly see that RAM is split into several regions. The STM32H723ZG device includes a built-in Ethernet MAC controller that uses DMA for its operation. It’s important to note that the DMA controller is in domain D2, meaning it cannot directly access memory in domain D1. Therefore, the linker script and source code must ensure that Ethernet DMA data structures are placed in domain D2; for example, in RAM_D2.

To achieve this, first define a section in the linker script and place it in the RAM_D2 region:

Second, the Ethernet driver source code must put respective data into that section. It may look like this:

Heap memory

The next important part is the microcontroller’s heap memory. The standard C library provides two basic functions for dynamic memory allocation:

Typically, ARM-based microcontroller SDKs are shipped with the ARM GCC compiler, which includes the Newlib C library. This library, like many others, has a concept of so-called “syscalls” featuring low level routines that user can override, and which are called by the standard C functions. In our case, the malloc() and free() standard C routines call the _sbrk() syscall, which firmware code can override.

It’s typically done in the sycalls.c or sysmem.c file, and may look this:

As we can see, the _sbrk() operates on a single memory region:

That means that such implementation cannot be used in several RAM regions. There are more advanced implementations, like FreeRTOS’s heap4.c, which can use multiple RAM regions and provides pvPortMalloc() and pvPortFree() functions.

In any case, standard C functions malloc() and free() provide heap memory as a shared resource. If several subsystems in a device’s firmware use dynamic memory and their memory usage is not limited by code, any of them can potentially exhaust the available memory. This can leave the device in an out-of-memory state, which typically causes it to stop operating.

Therefore, the solution is to have every subsystem that uses dynamic memory allocation operate within a bounded memory pool. This approach protects the entire device from running out of memory.

Memory pools

The idea behind a memory pool is to split a single shared heap—with a single malloc and free—into multiple “heaps” or memory pools, each with its own malloc and free. The pseudo-code might look like this:

The next step is to make each firmware subsystem use its own memory pool. This can be achieved by creating a separate memory pool for each subsystem and using the pool’s malloc and free functions instead of the standard ones.

In the case of a TCP/IP stack, this would require all parts of the networking code—driver, HTTP/MQTT library, TLS stack, and application code—to use a dedicated memory pool. This can be tedious to implement manually.

RTOS memory pool API

Some RTOSes provide a memory pool API. For example, Zephyr provides memory heaps:

The other example of an RTOS that provides memory pools is ThreadX:

Using external allocator

The other alternative is to use an external allocator. There are many implementations available. Here are some notable ones:

• umm_malloc is specifically designed to work with the ARM7 embedded processor, but it should work on many other 32-bit processors, as well as 16- and 8-bit processors.
• o1heap is a highly deterministic constant-complexity memory allocator designed for hard real-time high-integrity embedded systems. The name stands for O(1) heap.

Example: Mongoose and O1Heap

The Mongoose embedded TCP/IP stack makes it easy to limit its memory usage, because Mongoose uses its own functions mg_calloc() and mg_free() to allocate and release memory. The default implementation uses the C standard library functions calloc() and free(), but Mongoose allows user to override these functions with their own implementations.

We can pre-allocate memory for Mongoose at firmware startup, for example 50 Kb, and use o1heap library to use that preallocated block and implement mg_calloc() and mg_free() using o1heap. Here are the exact steps:

1. Fetch o1heap.c and o1heap.h into your source tree
2. Add o1heap.c to the list of your source files
3. Preallocate memory chunk at the firmware startup

4. Implement mg_calloc() and mg_free() using o1heap and preallocated memory chunk

You can see the full implementation procedure in the video linked at the end of this article.

Avoid memory exhaustion

This article provides information on the following design aspects:

• Understand STM32’s complex RAM layout
• Ensure Ethernet DMA buffers reside in accessible memory
• Avoid memory exhaustion by using bounded memory pools
• Integrate the o1heap allocator with Mongoose to enforce TCP/IP RAM limits

By isolating the network stack’s memory usage, you make your firmware more stable, deterministic, and secure, especially in real-time or resource-constrained systems.

If you would like to see a practical application of these principles, see the complete tutorial, including a video with a real-world example, which describes how RAM limiting is implemented in practice using the Mongoose embedded TCP/IP stack. This video tutorial provides a step-by-step guide on how to use Mongoose Wizard to restrict TCP/IP networking on a microcontroller to a preallocated memory pool.

As part of this tutorial, a real-time web dashboard is created to show memory usage in real time. The demo uses an STM32 Nucleo-F756ZG board with built-in Ethernet, but the same approach works seamlessly on other architectures too.

Sergey Lyubka is the co-founder and technical director of Cesanta Software Ltd. He is known as the author of the open-source Mongoose Embedded Web Server and Networking Library, which has been on the market since 2004 and has over 12K stars on GitHub. Sergey tackles the issue of making embedded networking simpler to access for all developers.

Related Content

• Developing Energy-Efficient Embedded Systems
• Can MRAM Get EU Back in the Memory Game?
• An MCU test chip embeds 10.8 Mbit STT-MRAM memory
• How MCU memory dictates zone and domain ECU architectures
• Breaking Through Memory Bottlenecks: The Next Frontier for AI Performance

The post How to limit TCP/IP RAM usage on STM32 microcontrollers appeared first on EDN.

Vishay Intertechnology, Inc. introduced a new high speed silicon PIN photodiode with enhanced sensitivity to visible and infrared light. Featuring a compact 3.2 mm by 2.0 mm top-view, surface-mount package with a low 0.6 mm profile, the Vishay Semiconductors VEMD8083 features high reverse light current and fast response times for improved performance in biomedical applications such as heart rate and blood oxygen monitoring.

The device offers a smaller form factor than previous-generation solutions, allowing for integration into compact wearables, such as smart rings, and consumer health monitoring devices. However, while its chip size is reduced, the photodiode’s package is optimized to support a large radiant sensitive area of 2.8 mm², which enables high reverse light current of 11 μA at 525 nm, 14 μA at 660 nm, and 16 μA at 940 nm.

The VEMD8083’s high sensitivity is especially valuable in biomedical applications like photo plethysmography (PPG), where it detects variations in blood volume and flow by measuring light absorption or reflection from blood vessels. Accurate detection in these scenarios is essential for diagnosing and monitoring conditions such as cardiovascular disease.

Pin to pin compatible with competing solutions, the device detects visible and near infrared radiation over a wide spectral range from 350 nm to 1100 nm. For high sampling rates, the VEMD8083 offers fast rise and fall times of 30 ns and diode capacitance of 50 pF. The photodiode features a ± 60° angle of half-sensitivity and an operating temperature range of -40 °C to +85 °C.

RoHS-compliant, halogen-free, and Vishay Green, the device provides a moisture sensitivity level (MSL) of 3 in accordance with J-STD-020 for a floor life of 168 hours.

Samples and production quantities of the VEMD8083 are available now.

The post New Vishay Intertechnology Silicon PIN Photodiode for Biomedical Applications appeared first on ELE Times.

thought it looked coo submitted by /u/PoweredBy555 [link] [comments]

This is more of a vent I guess. So maybe it’s because my workbench is in such disarray; my home office is trashed so I started doing work in the downstairs dining room and fucked that place up, too. Wreaking havoc around the house and the other half isn’t having it lol I’m trying to work on this board and wasn’t thinking about serviceability. Only after everything was done, I was like, “oh shit this thing better work”. Got everything wired up and proper, did point to point verification with a multimeter and resolved shorts on the 5v bus and come to find out, when powered on, the ESP32 is not working as expected. Everything is point to point soldered. So I need to rebuild this stupid thing from scratch, but the proper way using wire wrap techniques and socketing the ESP32 and logic level converter boards. Just FYI, this board I’m trying to build is meant to drive a HUB75 RGB panel with text/graphics from a Raspberry Pi’s UART interface. Prototype wise, it’s working as you can see in the background, now I’m trying to put everything on this perfboard as it is mean to be displayed in the open. The ESP32 is also driving 8 x MAX7219 8x8 LED matrix. This is an effort to build a thing centered around AI/LLM. My idea/concept got everyone in the AI community in an uproar, so I’m making an art piece out of it submitted by /u/Prijent_Smogonk [link] [comments]

Пам'яті Микити Купцова kpi ср, 11/12/2025 - 21:33

submitted by /u/Ready_Rain_2646 [link] [comments]

Cree LED Inc of Durham, NC, USA (a Penguin Solutions brand) has filed a patent infringement lawsuit in the US District Court for the Northern District of Illinois alleging that Promier Products (trading as LitezAll) and Tractor Supply Co have infringed its rights in the following patents by selling certain portable lighting products: United States Patent Nos. 9070850, 9754926, 10439112, 11791442, D790486 and D892066...

South Korea-based SK keyfoundry — which provides specialty analog and mixed-signal foundry services on 8-inch wafers for consumer, communications, computing, automotive and industrial applications — says that it is accelerating the development of silicon carbide (SiC)-based compound power semiconductor technology, bolstering its efforts for the global power semiconductor market. Leveraging its manufacturing expertise and intellectual property (IP) portfolio across the semiconductor manufacturing process, in first-half 2025 the firm acquired SK powertech (formerly Yes Power Technix, until its acquisition by SK Inc in 2022), which has core competencies in the SiC sector...

Під час ворожої атаки загинув випускник КПІ, фотограф і морпіх Костянтин Гузенко kpi ср, 11/12/2025 - 17:46

😉 Запрошення до публічного обговорення проєкту Положення про отримання та використання благодійної допомоги kpi ср, 11/12/2025 - 17:11

КПІшники — перші серед 787 команд на міжнародних змаганнях з кіберзбезпеки! KPI4U-2 ср, 11/12/2025 - 16:35

Blockchain Forensic Forum kpi ср, 11/12/2025 - 16:06

Infineon Technologies AG of Munich, Germany says that it is providing its gallium nitride (GaN) technology for the next generation of solar microinverters from global energy technology company Enphase Energy Inc, which is said to be the world’s leading supplier of microinverter-based solar and battery systems. Infineon’s CoolGaN bi-directional switch (BDS) technology enables significant enhancements in power output, energy efficiency and system reliability for Enphase’s IQ9 Series Microinverters. For the new IQ9N-3P Commercial Microinverter, this helps to simplify design complexity and lower installation and balance of system costs...

In the era of Industry 4.0, manufacturing is no longer defined solely by mechanical precision; it’s now driven by data, connectivity, and intelligence. Yet downtime remains one of the most persistent threats to productivity. When a machine unexpectedly fails, the impact ripples across the entire digital supply chain: Production lines stop, delivery schedules are missed, and teams scramble to diagnose the issue. For connected factories running lean operations, even a short interruption can disrupt synchronized workflows and compromise overall efficiency.

For decades, scheduled maintenance has been the industry’s primary safeguard against unplanned downtime. Maintenance was rarely data-driven but rather scheduled at rigid intervals based on estimates (in essence, educated guesses). Now that manufacturing is data-driven, maintenance should be data-driven as well.

Time-based, or ISO-guided, maintenance can’t fully account for the complexity of today’s connected equipment because machine behaviors vary by environment, workload, and process context. The timing is almost never precisely correct. This approach risks failing to detect problems that flare up before scheduled maintenance, often leading to unexpected downtime.

In addition, scheduled maintenance can never account for faulty replacement parts or unexpected environmental impacts. Performing maintenance before it is necessary is inefficient as well, leading to unnecessary downtime, expenses, and resource allocations. Maintenance should be performed only when the data says maintenance is necessary and not before; predictive maintenance ensures that it will.

To realize the promise of smart manufacturing, maintenance must evolve from a reactive (or static) task into an intelligent, autonomous capability, which is where Industry 4.0 becomes extremely important.

From scheduled service to smart systems

Industry 4.0 is defined by convergence: the merging of physical assets with digital intelligence. Predictive maintenance represents this convergence in action. Moving beyond condition-based monitoring, AI-enabled predictive maintenance systems use active AI models and continuous machine learning (ML) to recognize and alert stakeholders as early indicators of equipment failure before they trigger costly downtime.

The most advanced implementations deploy edge AI directly to the individual asset on the factory floor. Rather than sending massive data streams to the cloud for processing, these AI models analyze sensor data locally, where it’s generated. This not only reduces latency and bandwidth use but also ensures real-time insight and operational resilience, even in low-connectivity environments. In an Industry 4.0 context, edge intelligence is critical for achieving the speed, autonomy, and adaptability that smart factories demand.

AI-enabled predictive maintenance systems use AI models and continuous ML to detect early indicators of equipment failure before they trigger costly downtime. (Source: Adobe AI Generated) Edge intelligence in Industry 4.0

Traditional monitoring solutions often struggle to keep pace with the volume and velocity of modern industrial data. Edge AI addresses this by embedding trained ML models directly into sensors and devices. These models continuously analyze vibration, temperature, and motion signals, identifying patterns that precede failure, all without relying on cloud connectivity.

Because the AI operates locally, insights are delivered instantly, enabling a near-zero-latency response. Over time, the models adapt and improve, distinguishing between harmless deviations and genuine fault signatures. This self-learning capability not only reduces false alarms but also provides precise fault localization, guiding maintenance teams directly to the source of a potential issue. The result is a smarter, more autonomous maintenance ecosystem aligned with Industry 4.0 principles of self-optimization and continuous learning.

Building a future-ready predictive maintenance framework

To be truly future-ready for Industry 4.0, a predictive maintenance platform must seamlessly integrate advanced intelligence with intuitive usability. It should offer effortless deployment, compatibility with existing infrastructure, and scalability across diverse equipment and facilities. Features such as plug-and-play setup and automated model deployment minimize the load on IT and operations teams. Customizable sensitivity settings and severity-based analytics empower tailored alerting aligned with the criticality of each asset.

Scalability is equally vital. As manufacturers add or reconfigure production assets, predictive maintenance systems must seamlessly adapt, transferring models across machines, lines, or even entire facilities. Hardware-agnostic solutions offer the flexibility required for evolving, multivendor industrial environments. The goal is not just predictive accuracy but a networked intelligence layer that connects all assets under a unified maintenance framework.

Real-world impact across smart industries

Predictive maintenance is a cornerstone of digital transformation across manufacturing, energy, and infrastructure. In smart factories, predictive maintenance monitors robotic arms, elevators, lift motors, conveyors, CNC machines, and more, targeting the most critical assets in connected production lines. In energy and utilities, it safeguards turbines, transformers, and storage systems, preventing performance degradation and ensuring safety. In smart buildings, predictive maintenance monitors HVAC systems and elevators for advanced notice of needed maintenance or replacement of assets that are often hard to monitor and cause great discomfort and loss of productivity during unexpected downtime.

The diversity of these applications underscores an Industry 4.0 truth: Interoperability and adaptability are as important as intelligence. Predictive maintenance must be able to integrate into any operational environment, providing actionable insights regardless of equipment age, vendor, or data format.

Intelligence at the industrial edge

The edgeRX platform from TDK SensEI, for example, embodies the next generation of Industry 4.0 machine-health solutions. Combining industrial-grade sensors, gateways, dashboards, and cloud interfaces into a unified system, edgeRX delivers immediate visibility into machine-health conditions. Deployed in minutes, it immediately begins collecting data to build ML models for deployment from the cloud back to the sensor device for real-time inference on the sensor at the edge.

By processing data directly on-device, edgeRX eliminates the latency and energy costs of cloud-based analytics. Its ruggedized, IP67-rated hardware and long-life batteries make it ideal for demanding industrial environments. Most importantly, edgeRX learns continuously from each machine’s unique operational profile, providing precise, actionable insights that support smarter, faster decision-making.

TDK SensEI’s edgeRX advanced machine-health-monitoring platform (Source: TDK SensEI) The road to autonomous maintenance

As Industry 4.0 continues to redefine manufacturing, predictive maintenance is emerging as a key enabler of self-healing, data-driven operations. EdgeRX transforms maintenance from a scheduled obligation into a strategic function—one that is integrated, adaptive, and intelligent.

Manufacturers evaluating their digital strategies should ask:

• Am I able to remotely and simultaneously monitor and alert on all my assets?
• Are our automated systems capturing early, subtle indicators of failure?
• Can our current solutions scale with our operations?
• Are insights available in real time, where decisions are made?

If the answer is no, it’s time to rethink what maintenance means in the context of Industry 4.0. Predictive, edge-enabled AI solutions don’t just prevent downtime; they drive the autonomy, efficiency, and continuous improvement that define the next industrial revolution.

The post Predictive maintenance at the heart of Industry 4.0 appeared first on EDN.