Microelectronics world news

Renesas Electronics Corp of Tokyo, Japan has expanded its gallium nitride (GaN) portfolio into low-voltage applications with its first family of 100V enhancement-mode (E-mode) GaN-based discrete power transistors. The RTP100E005G1FL, RTP100E2P6G1FL, RTP100E1P8G1FL-DSC and RTP100E1P2G1FL-DSC low-voltage GaN FETs are said to deliver ultra-fast switching speeds and enhanced thermal performance in efficiency-critical, high-power-density applications, including AI data centers, humanoid robotics, factory automation and industrial motor drives, power tools and solar micro-inverters...

A portable square wave generator covering from 2 Hz to about 33.5 MHz, with adjustment steps of 2 Hz. The heart of the project is an AVR16EB28 microcontroller, which handles both signal generation and the user interface. Power is supplied by a LiPo battery, while an OLED display, rotary encoder, and push-button keypad provide full control. The project is by David Johnson-Davies, known for his experiments with AVR microcontrollers.

The frequency is set with precision, and the reading appears on the OLED display. The rotary encoder allows rapid variations, while the keypad is used to enter exact values. The whole thing fits in a compact enclosure, suitable for the workbench or the field. The 2 Hz resolution across the entire range is remarkable, and makes the device useful for testing audio circuits, filters, and timing.

Circuit and control with AVR16EB28

The schematic is simple: the AVR16EB28 microcontroller generates the square wave directly from a pin, with the frequency calculated in software. Control is via an OLED display, rotary encoder, and push-button keypad. The LiPo battery powers the whole system, with a regulator for a stable voltage. The project is designed to be replicated with easily available components.

The firmware handles the 2 Hz steps and updates the display in real time. In addition, the rotary encoder allows scrolling through frequencies smoothly, while the keypad allows direct entry of a value. The code is available on the maker’s website, and includes libraries for the display and encoder. The result is a stable and repeatable device.

Construction, power, and practical use

Construction requires a PCB, which can be made with a milling machine or through an external service. Assembly is within reach of those with SMD soldering experience, since the microcontroller is in a surface-mount package. The LiPo battery connects on the back, and the front panel hosts the display, encoder, and keypad. The whole thing is compact and easily portable.

For power, a 3.7 V LiPo battery is sufficient, with a voltage regulator for the 3.3 V of the microcontroller. Consumption is low, thanks to the OLED display and efficient sleep management. Practical use is immediate: turn it on, select the frequency, and connect the output to the circuit under test. The precision of the 2 Hz steps makes it suitable even for fine adjustments.

David Johnson-Davies’s website hosts the source code and construction details. Those who want to go deeper can consult the complete documentation, including schematics and assembly photos. The project demonstrates how a modern AVR microcontroller can generate high-frequency signals with precision, without complex external components. An elegant solution for those seeking a reliable square wave generator.

In summary, this square wave generator offers a wide range and fine resolution, all in a portable format. The choice of an AVR16EB28 ensures programming simplicity and low cost. The OLED display and manual controls make it intuitive to use, even for those unfamiliar with professional instruments. A project worth replicating.

Source: http://www.technoblogy.com/show?5QE2

The post Square Wave Generator from 2 Hz to 33.5 MHz with AVR16EB28 appeared first on Open Electronics.

As AI data centers scale to support high-power GPU clusters, the industry is shifting toward 800V DC rack power architectures to improve distribution efficiency, increase power density and support next-generation server designs. To help accelerate this transition, Microchip Technology and Navitas Semiconductor (Nasdaq: NVTS) have collaborated on an 800V DC-to-6V DC reference design for AI data center rack power applications.

The platform combines Microchip’s digital power control and security technologies with Navitas’ GaNFast gallium nitride (GaN) power devices to give developers a practical path to implement high-efficiency power conversion aligned with the Open Compute Project (OCP) 800V DC standard. Complete with reference hardware, software and design documentation, the solution helps reduce design risk, shorten development cycles and accelerate deployment of next-generation AI infrastructure.

“AI infrastructure optimization is driving one of the most significant power architecture transitions the data center industry has experienced in decades,” said Joe Thomsen, corporate vice president of Microchip’s digital signal controller business unit. “As the ecosystem moves toward higher-voltage rack power systems, developers need proven control and security to help reduce implementation risk. Our collaboration with Navitas combines digital control, hardware-based security and advanced GaN power conversion to help customers bring 800V rack power systems to market more quickly.”

At the core of the reference platform are Microchip’s dsPIC33AK Digital Signal Controllers (DSCs) and TA100 CryptoAuthentication security IC, paired with Navitas’ GaNFast FETs. The dsPIC33AK provides deterministic digital power control for high-frequency, high-efficiency DC/DC conversion, while the TA100 helps establish a hardware root of trust for authentication, secure boot and protected firmware updates. Together, these technologies make up the precision control and security foundations required for connected OCP power supply designs. The dsPIC33AK256MPS306 family is powered by a 200 MHz 32-bit core with a double-precision floating-point unit (FPU), 78 ps high-resolution Pulse Width Modulators (PWMs) and multiple 12-bit Analog-to-Digital Converters (ADCs) operating at up to 40 MSPS. The devices include library support for Commercial National Security Algorithm (CNSA) Suite 2.0 recommended post-quantum cryptographic algorithms and hardware-accelerated cryptographic functions for connected real-time control designs.

This PDB is powered by 16 × NV6034, 650 V, 17 mΩ GaNFast FETs in a stacked half-bridge topology on the primary side. The DFN8×8 dual-side-cooled package extends the performance advantages of GaN by reducing thermal resistance, allowing higher continuous power operation while maintaining exceptional efficiency. The PDB targets delivering up to 96% peak efficiency at full load with 1 MHz switching frequency, enabling a power density of 2,100 W/in³.

Approximately 20% thinner than a mobile phone, its ultra-low profile enables extremely close integration with the GPU board, maximizing transient performance and improving power distribution efficiency. Navitas’ system-level approach helps translate advances in GaN technology into measurable improvements in efficiency, power density, transient performance and total cost of ownership. Direct conversion from 800V DC to 6V DC combines both 800V DC to 50V DC and 50V DC to 6V DC conversion stages into one converter, delivering higher end-to-end efficiency.

“As AI infrastructure scales to support increasingly demanding computing platforms, Navitas’ GaNFast technology is a critical enabler of higher power density, greater efficiency and improved system performance,” said Vipin Bothra, vice president of Global Solution Marketing at Navitas Semiconductor. “By combining Navitas’ leadership in power semiconductors with Microchip’s digital control expertise, this collaboration accelerates the delivery of advanced power solutions tailored to the evolving requirements of next-generation AI data centers.”

Hardware-based security is integrated through Microchip’s TA100 CryptoAuthentication IC, enabling developers to establish a trusted foundation for system authentication and protection without implementing these capabilities from scratch. The TA100 device provides support for code authentication, including secure boot, Message Authentication Code (MAC) generation, trusted firmware updates, multiple key management protocols including Transport Layer Security (TLS), and other root-of-trust-based operations.

The Microchip and Navitas reference design provides a platform for developing high-voltage, high-power, compact rack power systems for AI data centers. The design is supported with a reference board, software and documentation, giving developers access to the resources needed to evaluate and accelerate deployment of 800V DC power conversion systems for AI data center applications.

The post The Data Center Is Moving to 800V: Microchip and Navitas Are Enabling the Transition appeared first on ELE Times.

​AI is leaving the question-answering and content-generation stage. The frontier of the next generation of AI is agentic AI. That’s an AI that perceives an objective, reasons about the problem by planning, compares multi-step plans, accesses external tools, and acts independently without a human operator. Moving from a stand-alone question-answering bot that responds to a prompt to an agent to execute a task means asking enterprise data stories, working with platforms, running workflows, testing and accessing outputs, and changing the data. By 2026, enterprises will shift from publishing generative AI experiments and exploration to launching agentic workflows-connecting foundation models to enterprise data, programs, and operations.

What Is Agentic AI?

Agentic AI is the type of AI that is trying to obtain a goal and perform a multi-step task on its own. Rather than requiring someone to make every decision for it, an agent can decompose a goal, develop a plan to attack it, determine what tools are going to be needed, do the work, and evaluate the results. A traditional AI assistant, for instance, might answer a question about whether a customer’s order is ready. An agentic AI would be able to retrieve the order details, identify that the order is held up, tap into the source system, compose an updated message to the customer, and create a case for escalation to a human customer support representative when necessary.

How Does Agentic AI Work?

So, in essence, it’s just a perception, reasoning, planning, and acting loop: A goal or task is communicated or perceived by the agent; the agent gathers data from the environment (databases, files, APIs, enterprise applications, API or other connected systems); the agent reasons with its AI model and takes actions. The agent further decomposes the goal into intermediate, achievable steps (if needed) through a set of tools that it gathers. If the step wasn’t completed successfully or if the conditions change, the agent updates the plan and repeats. Put simply, the working process of an agentic automation is something like goals, perception, reasoning, planning, tool use, action, evaluation, next action. This continuous agentic loop is what sets agentic AI apart from automation that relies on us anticipating all potential options and expressing every possible path in advance.

Agentic AI Architecture and Its Components

The architecture of a production-grade agentic AI system contains multiple layers. The first layer, that of the agents themselves, can include an AI model, such as a large language model or other foundation model that can comprehend instructions, assess context, apply reasoning, and figure out what actions can be taken. Businesses can choose various models, based on a task’s complexity, latency, cost, privacy needs, or safety. But the model is just the start of a good agentic system.

An orchestrator controls how the agent will go about completing a task. An orchestrator can decide the order of operations, control context, route information, coordinate tool calls, and manage multiple specialised agents working together. This is especially critical when an enterprise task involves multiple steps and multiple agents or multiple AI agents working together. An orchestrator offers the structure around model logic and allows organisations to manage agent-to-business system interactions.

Tools and APIs supply the AGI agent with the ability to act. A tool might be a database, enterprise search engine, API, CRM, and ERP systems, software development environment, code execution platform, communication channel, or almost any enterprise system. Without the ability to access the tools, the basic AI model is capable of modifying or creating data. With the ability to do so within a closed environment, the agent can access data and perform the appropriate activities.

Knowledge and grounding are another aspect of architecture to consider. It’s not wise to depend solely on the information within a foundation model; that’s when your enterprise agents need factually accurate and pertinent knowledge. Retrieval augmented generation, enterprise knowledge bases, semantic search, structured data, and application data can all supply this context, grounding an agent in enterprise information and decreasing the potential for generating contradictory outputs.

Memory and context enable an agent to remember information during a task or conversation, depending on your implementation. Short-term memory can give the agent a sense of how a current conversation or task is proceeding, and long term memory might contain information that will be useful for some future task. However, be cautious when considering memories in the enterprise; agents will have access to private data regarding customers, financial data, operations, and employees.

Security and governance are another critical architecture level. An autonomous system that has business systems and skills to act must be granted the right. An agent’s authority and role-based access control, the principle of least privilege, the ability to be audited, observability, and monitoring, policies, human-in-the-loop, and safety guardrails reduce the likelihood of an agent taking an unauthorised action. As enterprise AI becomes ever more autonomous, it is becoming an architectural requirement.

What Are Agentic Workflows?

Agentic workflows are flexible sequences in which agents think, plan, carry out multiple steps, assess results, and adapt appropriately. For example, at an IT-support desk, an agent could not only give instructions on how to fix an employee’s malfunctioning app but could also diagnose the issue, survey the computer’s activity logs, review recent changes to its settings, identify likely causes, offer a fix, and- with permission- carry out the fix. The agent could check whether the app’s restored to normal.

In a higher-level workflow, there may be a team of agents; one particular expert agent may go through the technical solution, another cybersecurity agent may look into potential security issues, and another could look at the solution details prior to the ultimate actions being approved. This is the multi-agent system and allows an organisation to distribute many complex workflow jobs over a number of specialist AI agents while maintaining overall control.

Enterprise Use Cases of Agentic AI

The scope of enterprise agentic AI use cases is growing fast. In customer service, for instance, agentic AI can help employees by classifying support requests, loading customer information, troubleshooting frequent issues, generating responses, recording updates, and escalating sophisticated cases to human agents. This doesn’t mean, however, that they will replace human support teams. Instead, they can automate mundane workflows and free employees to focus on more nuanced, human interactions.

An additional significant domain is software development. In agentic coding systems, AI could possibly investigate and alter web pages, generate or suggest code, run and review code, test and examine bugs, and suggest or make fixes. This is a shift from AI as a coding assistant that shows code snippets to AI programs that can perform many stages of the software development process.

Agentic Automation in security: Security is a good fit for agentic work because you wouldn’t want a security team to look at lots of alerts and aggregation points. The agents can track activity, investigate anomalies, link information, recognize attacks, specify a response, and in certain cases take predefined remediation actions. But for high-consequence actions, they need to have limited authority, be approved, audited, and reviewed by humans.

Enterprise AI agents can also perform document analysis, fraud investigations, compliance processing, customer service, research, and any other enterprise activity involving a large set of business data. For example, supply-chain agents can work out whether a supply disruption is imminent using knowledge of suppliers’ status, inventory position, demand, and logistics, and then suggest remedial measures for procurement, inventory, and logistics, and so on.

Healthcare is yet another option, mainly for administrative work such as data entry, making appointments, finding the right data, and road-mapping the workflow. AI’s use for clinical purposes has to go through a stronger validation process, as the wrong decision on the part of the AI could directly endanger patients’ lives.

Why Enterprise Agentic AI Needs Strong Governance

There are also risks associated with using an autonomous agentic AI. Entering an instruction wrong, not using the correct tool, escaping, leaking, or bending a malicious prompt are all ways to the dark side. Failure can cascade across agents in multi-agent systems and to the wider systems. Companies should regard AI agents as operational software rather than a new chat service.

Good governance might include implementing identity and access management, least-privilege permissions, requiring approvals for high-risk actions, tracking and logging actions with audit trails, providing data-protection controls, immediate defence against injection attacks, model assessment, and validation of tools and controls on resources. Also, enterprises will need accountability and ownership of what AI agents are doing. So, the new model will not be one of total freedom but controlled freedom.

The Future of Agentic AI

The advent of agentic AI will probably see the development of more specialised agents within shared enterprise IT systems. Rather than a generalist to do all types of jobs, firms will deploy specialist agentic models in software engineering, customer service, cybersecurity, finance, supply chain and more. They will operate alongside each other on shared levels of orchestration, identity, observability and governance.

This shift is also transforming how organisations conceptualize their enterprise software landscape. More and more, AI agents are being considered an operational layer that communicates with the applications already in place- rather than an expansion that necessitates replacements for every single back-end application. As agentic workflows develop further, tools such as agent registries, observability tools, policy engines, security measures, and standards for interoperability may come into play.

So, the big change, therefore, is not so much between chatbots and autonomous AI. It also falls somewhere in the middle of AI as a set of features in the software process beneath. Enterprises that can master the art of good model construction, having access to high-quality data, appropriate tooling, targeted orchestration, and appropriate governance, will be the ones to benefit from agentic AI.

Conclusion

Agentic AI is the future of enterprise AI. By integrating data, orchestration, and governance with reasoning models, enterprise AI agents become everlasting multi-step workers and not static question-answering tools. Use cases for agentic AI are emerging in customer service. Software engineering, security, finance, healthcare, and supply chain.

And that smart model isn’t enough to make it enterprise-ready. Security, permission, observability, reliability, interoperability, and human oversight will all play a role as organisations consider whether to gate agents’ transition from pilots to production. As companies start to reorganise their workflows around autonomous and semi-autonomous systems, technology leaders should have a firm grasp of agentic AI architecture, its underlying components, how it operates, and its enterprise use cases.

The post What Is Agentic AI ? Architecture, Components, Workflows, and Enterprise Use Cases appeared first on ELE Times.