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Only two components, a esp32 board & 0.96 inch oled screen, blue is the 0.96 inch oled screen & black is the esp32 with USB-C submitted by /u/llo7d [link] [comments]

In this article, we examine two different methods of producing wideband FM signals. Both use crystal oscillators to provide improved frequency stability.

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Introduction: The “Old” Tech Powering the “New” World

In today’s electronics ecosystem, conversations are dominated by artificial intelligence, edge computing, and ultra-fast wireless networks. Yet, behind every groundbreaking innovation, there lies a quieter but indispensable player i.e., analog electronics. While digital may dominate headlines, it is analog that ensures real-world phenomena which can be captured, conditioned, and processed.

As engineers often remind themselves, “Nature is analog. Everything else is an approximation.” No matter how sophisticated a digital system is, its accuracy and reliability hinge on the quality of the analog front-end. From radar in advanced driver-assistance systems (ADAS) to MEMS accelerometers in smartphones, and from biomedical wearables to industrial IoT nodes, analog electronics forms the first link in the sensor signal chain.

Why All Sensors Speak Analog First

Every physical phenomenon light intensity, sound waves, heat, vibration, or radio frequency (RF) radiation exists in analog form. Sensors are essentially transducers, converting these continuous signals into measurable electrical quantities. But before such data can be digitized and analyzed by processors or AI algorithms, it must pass through an analog front-end (AFE).

The AFE includes key blocks such as instrumentation amplifiers, filters, linearization circuits, and signal conditioning modules that prepare raw sensor outputs for analog-to-digital conversion (ADC). Without robust analog conditioning, even the most advanced digital processors would be “blind” to the real world.

As Walt Maclay, CEO of Voler Systems, puts it: “Digital processing is only as good as the analog electronics feeding it. Garbage in, garbage out applies more to sensors than anywhere else.”

Core Functions of Analog in Modern Sensors

1. Signal Amplification
   Many sensors output signals in the microvolt or millivolt range, easily drowned by noise. Instrumentation amplifiers and low-noise amplifiers (LNAs) boost these signals while preserving fidelity. For example, electrocardiogram (ECG) sensors require amplifiers with high common-mode rejection ratios (CMRR) to extract meaningful heart signals from noise-laden environments.
2. Filtering
   Real-world signals are messy. Analog active and passive filters remove unwanted noise and interference before digitization. In radar systems, bandpass filters ensure only the target frequency range is processed, dramatically improving signal-to-noise ratio (SNR).
3. Linearization & Biasing
   Many sensor outputs are nonlinear by nature. Analog circuits implement linearization techniques that correct these distortions, making sensor behavior predictable. Similarly, biasing ensures transducers operate in optimal ranges. For example, in thermistors, resistance-to-temperature curves must be linearized before meaningful temperature data is derived.
4. Conversion Readiness
   Analog circuits prepare signals for ADC compatibility by ensuring proper voltage levels, impedance matching, and bandwidth. Without this step, digitization could lead to clipping, aliasing, or resolution loss.

Case Studies: Analog at Work in Emerging Applications

1. Automotive ADAS

ADAS relies heavily on radar and LiDAR sensors, where real-time performance is non-negotiable. Analog front-ends amplify weak RF echoes, filter them for interference, and feed precise signals to high-speed ADCs. Even a microsecond delay can mean the difference between safe braking and a collision.

2. Biomedical Devices

Wearable medical devices like glucose monitors and ECG patches demand ultra-low-power, high-precision analog circuits. Here, analog electronics extend battery life while ensuring clinical-grade accuracy. An error of even 1 mV in amplification could translate into misdiagnosis.

3. Industrial IoT

Factories rely on thousands of sensors for vibration monitoring, predictive maintenance, and process automation. Analog circuits in these environments must withstand electrical noise, temperature fluctuations, and mechanical stress. Unlike fragile digital logic, robust analog designs ensure reliability under extreme industrial conditions.

4. Environmental Monitoring

Long-term stability is critical in air-quality monitors, soil sensors, or weather stations. Analog circuits designed for low drift and high linearity guarantee consistent data for years without recalibration.

Analog’s Edge Over Digital in Certain Tasks

While digital processing offers flexibility, analog holds an edge in critical aspects:

• Zero Latency: Analog signals propagate at the speed of physics — no clock cycles required. For radar-based collision avoidance, this deterministic performance is irreplaceable.
• Power Efficiency: Analog front-ends consume far less power than equivalent digital circuits, making them essential in wearables and IoT nodes where every microamp counts.
• Reliability under Harsh Conditions: Analog circuits continue functioning in extreme environments — radiation, high temperatures, or electromagnetic interference — where digital logic often fails.

As Bob Dobkin, co-founder of Linear Technology, famously said: “Analog will never die, because the world is analog.”

Integration Trends: Analog in the Age of SoCs and SiPs

The industry is increasingly moving towards system-on-chips (SoCs) and system-in-packages (SiPs) that integrate both analog and digital functions. For instance, today’s MEMS inertial sensors often include on-chip AFEs, ADCs, and digital processors in a single package. This integration reduces footprint, improves signal integrity, and supports miniaturization for wearables, drones, and autonomous systems.

However, integration does not eliminate the need for analog expertise. Instead, it requires engineers to design mixed-signal systems where the interplay between analog and digital domains is carefully managed. Issues like thermal drift, bandwidth matching, and parasitic effects remain squarely in the analog domain.

Conclusion: Analog as the Permanent Foundation

In the race towards digital transformation, analog electronics is often overlooked. Yet, it is precisely analog that determines how effectively digital systems can sense and respond to the physical world. Whether in self-driving cars, medical diagnostics, or industrial automation, analog remains the timeless backbone of modern sensors.

For engineers, the message is clear: mastering analog design is not a relic skill, but a future-proof investment. The more complex and interconnected systems become, the more critical it is to ensure rock-solid analog foundations.

As the electronics pioneer Barrie Gilbert once noted: “You can digitize data, but you cannot digitize reality. Reality is, and will always be, analog.”

The post Analog Electronics: The Timeless Backbone of Modern Sensors appeared first on ELE Times.

I was playing with 12v LED cob panels and wanted to drive them from a esp32 on breadboard. So i made this, with 6 2N7000 mosfets and the associated resistors. I was quite pleased with my happy notion of alternating the orientation of the transitors alternately so the sources were all in a line, this also made the drains form neat pairs. which was nice. submitted by /u/Open_Theme6497 [link] [comments]

Hello everyone, Chris here. I built a simple TCI ignition module, and it works— but I haven’t tested it yet on a motorcycle or anything else. My friend said he had done this before on a classic car and it worked. I’ve uploaded a full tutorial video with the circuit and parts on YouTube. You can check it out and let me know what you think— I’ll put the link in the comments. submitted by /u/mrwolfdiy [link] [comments]

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I want to make my own PCBs, but i find all the PCB design programs infuriating. So i have been honing my free hand skills, using blank copper clad board and an etch resistant pen. This, a simple 555 flasher, is my latest one. I used a SOIC 555 with 0805, and 0603 surface mount supporting components. submitted by /u/Open_Theme6497 [link] [comments]

In 2021, car manufacturers worldwide halted production because a single one-dollar microcontroller was unavailable. The wait time for advanced semiconductors jumped from 12 weeks to over 26 weeks, revealing how fragile the global supply chain had become. The yield losses and manufacturing defects are not just technical issues-they are strategic challenges affecting procurement leaders, supply chain managers, and even national economies.

Meanwhile, demand for semiconductors continues to grow relentlessly. Global consumption is expected to increase at a compound annual growth rate of 7 to 8 percent through 2030, while production capacity is only growing at about 5 percent per year. This mismatch makes every wafer incredibly valuable. Even a modest 2 percent improvement in yields at advanced technology nodes could free up around 150,000 wafers annually, which translates into billions of dollars of extra supply.

Generative AI addresses these challenges by creating optimized designs in advance, anticipating potential defects, and enhancing scheduling in wafer fabrication. It is reshaping the economics of the semiconductor industry- improving yields, reducing inconsistencies, and strengthening supply chains’ reliability.

The Yield Challenge in Semiconductor Manufacturing

Chip manufacturing involves more than 1,000 steps, from photolithography to etching. At advanced nodes of three nanometres and below, tiny atomic-level variations can make wafers unusable. With single-wafer costing over 16,000 dollars, any loss in yield directly cuts profit margins.

Every percentage point of yield improvement is like adding a new fabrication plant without capital investment, said Sanjay Mehrotra, CEO of Micron Technology.

How Generative AI Creates Strategic Value

Generative models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and foundation models go beyond predictive analytics:  they generate better alternatives. Four applications stand out:

1. Design Optimization

Generative AI evaluates thousands of layout variations to identify configurations that reduce defects. Synopsys, working with Taiwan Semiconductor Manufacturing Company (TSMC), reported a 15 percent yield improvement using AI-driven design space exploration.  Faster design cycles and quicker delivery to customers follow.  A European fabless design company leveraged generative AI for design optimisation and achieved ROI in just 18 months, reducing wafer scrap, accelerating revenue realization, and lowering operational costs.

2. Defect Prediction

AI generates synthetic wafer maps to train inspection systems before defects appear. American-based KLA corporation reported 25–30 percent improvement in defect detection, resulting in more usable wafers and faster production cycles.  Samsung implemented AI-based yield learning to cut line failure rates by 12 percent, decreasing buffer inventory needs and improving delivery reliability.

3. Assistance with Lithography

AI supports mask patterns generation to minimize distortions through Inverse Lithography Technology (ILT) and Optical Proximity Correction (OPC). Intel reported a 40 percent reduction in edge-placement error, increasing first-pass yields.

4. Supply Assurance and Fabric Scheduling

Generative AI simulates thousands of scheduling scenarios, balancing tool usage, and maximizes throughput.  A Taiwanese fabless company reduced wafer cycle times from 20 to 17 days using AI scheduling, ensuring timely chip delivery in a competitive market.

It also strengthened broader supply chain resilience. Global Foundries applied predictive analytics to reduce recovery times during material shortages by 30 percent, helping procurement meet client demand during disruptions.

Industry Case Studies and Outcomes

• Samsung Foundry – AI-based Yield Learning- It reduced the cut line failure rates by 12 percent, lowering buffer inventory requirements and improving delivery reliability for customers.
• Global Foundries – Predictive Supply Chain Analytics: Using predictive analytics, it improves supply chain resilience and cuts recovery times during material shortages by 3 percent, enabling procurement teams to meet client demands.
• European Fabless Design Company – Design Optimisation: Employing generative AI for layout optimisation, the company achieved return on investment (ROI) in just 18 months. By decreasing wafer scrap, speeding revenue realisation, and reducing operational cost.

 Strategic Procurement and Supply Chain Value

Generative AI serves the dual role. On the shop floor, it functions like examining billions of flaw patterns to increase yields. In the boardroom, it mitigates risk, strengthens supply continuity, and protects margin.
Predictive insight facilities by generative AI can help with lead time optimisation, multi-sourcing strategy guidance, and supplier negotiations, and align contractual requirements with actual fab performance, ensuring reliable capacity guarantees.

SEMI CEO Ajit Manocha stated that generative AI is not just yield enhancement-, it lowers process variability, increases predictability, and strengthens overall operational resilience.

Challenges to Adoption

Despite its transformative potential, adopting generative AI in the semiconductor industry presents several challenges:

Ø   Data confidentiality:  It remains the key concern because the processed data is so proprietary and difficult to share across ecosystems.

Ø   Computational intensity: It requires a substantial amount of computational equipment to train sophisticated AI generative models.

Ø   Explainability gaps: To foster confidence, engineers and procurement teams need AI advice to be transparent.

Ø  Change management: To fully realise value, Fabs must retrain process engineers, educate procurement specialists in AI literacy, and link data science teams across silos.

The Road Ahead: Toward Autonomous and Resilient Fabs

Next-generation semiconductor factories are increasingly relying on generative AI as central intelligence. Emerging trends include:

1. Autonomous fabs:  It leverages generative AI to modify recipes in real time to reduce yield loss and improve efficiency.
2. Collaborative ecosystems: Design firms, equipment manufacturers, and fabs share AI models to optimize production and supply chain resilience.
3. Zero-defect manufacturing: While idealistic, generative AI is making substantial progress towards achieving it, bringing fabs closer to near-perfect yield and consistency.

Strategic Imperatives for Leaders

The path forward is clear for procurement executives, semiconductor leaders, and strategy decision makers:

1. Scale AI across operations: Transition from pilots to full integration in scheduling, lithography, electronic design automation, and inspection workflow.
2. Leverage AI in procurement: Use insights for contract negotiations, supplier diversification, and lead time predictability.
3. Invest in people and collaborations:  Integrate the expertise of supply chain managers, data scientists, and strengthen collaboration with AI solution providers and academic institutions.

Conclusion

Generative AI is transforming chip manufacturing. It boosts yields, cuts defects, and improves production scheduling. More importantly, it helps leaders make supply chains stronger, margins steadier, and delivery times more predictable.

Companies that embrace AI first will unlock extra capacity, protect supply continuity, and gain a clear competitive edge. Every wafer counts, and every week of lead time matters. Generative AI ensures neither is wasted.

The post Generative Artificial Intelligence Boosts Chip Yields and Slashes Manufacturing Defects appeared first on ELE Times.