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Fan-out wafer-level packaging (FOWLP) is becoming a critical technology in advanced semiconductor packaging, marking a significant shift in system integration strategies. Industry analyses show 3D IC and advanced packaging make up more than 45% of the IC packaging market value, underscoring the move to more sophisticated solutions.

The challenges are significant—from thermal management and testing to the need for greater automation and cross-domain expertise—but the potential benefits in terms of performance, power efficiency, and integration density make these challenges worth addressing.

Figure 1 3D IC and advanced packaging make up more than 45% of the IC packaging market value. Source: Siemens EDA

This article explores the automation frameworks needed for successful FOWLP design and focuses on core design processes and effective cross-functional collaboration.

Understanding FOWLP technology

FOWLP is an advanced packaging method that integrates multiple dies from different process nodes into a compact system. By eliminating substrates and using wafer-level batch processing, FOWLP can reduce cost and improve yield. Because it shortens interconnect lengths, FOWLP packages offer lower signal delays and power consumption compared to conventional methods. They are also thinner, making them ideal for space-constrained devices such as smartphones.

Another key benefit is support for advanced stacking, such as placing DRAM above a processor. As designs become more complex, this enables higher performance while maintaining manageable form factors. FOWLP also supports heterogeneous integration, accommodating a wide array of die combinations to suit application needs.

The need for automation in FOWLP design

Designing with FOWLP exceeds the capabilities of traditional PCB design methods. Two main challenges drive the need for automation: the inherent complexity of FOWLP and the scale of modern layouts, racking up millions of pins and tens of thousands of nets. Manual techniques cannot reliably manage this complexity and scale, increasing the risk of errors and inefficiency.

Adopting automation is not simply about speeding up manual tasks. It requires a complete change in how design teams approach complex packaging design and collaborate across disciplines. Let’s look at a few of the salient ways to make this transformation successful.

1. Technology setup

All FOWLP designs start with a thorough technology setup. Process design kits (PDKs) from foundries specify layer constraints, via spans, and spacing rules. Integrating these foundry-specific rules into the design environment ensures every downstream step follows industry requirements.

Automation frameworks must interpret and apply these rules consistently throughout the design. Success here depends on close attention to detail and a deep understanding of both the foundry’s expectations and the capabilities of the design tools.

2. Assembly and floor planning

During assembly and floor planning, designers establish the physical relationships between dies and other components. This phase must account for thermal and mechanical stress from the start. Automation makes it practical to incorporate early thermal analysis and flag potential issues before fabrication.

Effective design partitioning is also critical when working with automated layouts. Automated classification and grouping of nets allow custom routing strategies. This is especially important for high-speed die-to-die interfaces, compared to less critical utility signals. The framework should distinguish between these and apply suitable methodologies.

3. Fan-out and routing

Fan-out and routing are among the most technically challenging parts of FOWLP design. The automation system must support advanced power distribution networks such as regional power islands, floodplains, or striping. For signal routing, the system needs to manage many constraints at once, including routing lengths, routing targets, and handling differential pairs.

Automated sequence management is essential, enabling designers to iterate and refine routing as requirements evolve. Being able to adjust routing priorities dynamically helps meet electrical and physical design constraints.

4. Final verification and finishing

The last design phase is verification and finishing. Here, automation systems handle degassing hole patterns, verifying stress and density requirements, and integrating dummy metal fills. Preparing data for GDS or OASIS output is streamlined, ensuring the final package meets manufacturing and reliability standards.

Building successful automated workflows

For FOWLP automation flows to succeed, frameworks must balance technical power with ease of use. Specialists should be able to focus on their discipline without needing deep programming skills. Automated commands should have clear, self-explanatory names, and straightforward options.

Effective frameworks promote collaboration among package designers, layout specialists, signal and power integrity analysts, and thermal and mechanical engineers. Sharing a common design environment helps teams work together and apply their skills where they are most valuable.

A crucial role in FOWLP design automation is the replay coordinator. This person orchestrates the entire workflow, managing contributions from all team members as well as the sequence and dependencies of automated tasks, ensuring that all the various design steps are properly sequenced and executed.

To be effective, replay coordinators need a high-level understanding of the overall process and strong communication with the team. They are responsible for interpreting analysis results, coordinating adjustments, and driving the group toward optimal design outcomes.

The tools of the new trade

This successful shift in how we approach microarchitectural design requires new tools and technologies that support the transition from 2D to 3D ICs. Siemens EDA’s Innovator3D IC is a unified cockpit for design planning, prototyping, and predictive analysis of 2.5/3D heterogeneous integrated devices.

Innovator3D IC constructs a digital twin, unified data model of the complete semiconductor package assembly. By using system technology co-optimization, Innovator3D IC enables designers to meet their power, performance, area, and cost objectives.

Figure 2 Innovator3D IC features a unified cockpit. Source: Siemens EDA

FOWLP marks a fundamental evolution in semiconductor packaging. The future of semiconductor packaging lies in the ability to balance technological sophistication with practical implementation. Success with this technology relies on automation frameworks that make complex designs practical while enabling effective teamwork.

As industry continues to progress, organizations with robust FOWLP automation strategies will have a competitive advantage in delivering advanced products and driving the next wave of semiconductor innovation.

Todd Burkholder is a Senior Editor at Siemens DISW. For over 25 years, he has worked as editor, author, and ghost writer with internal and external customers to create print and digital content across a broad range of EDA technologies. Todd began his career in marketing for high-technology and other industries in 1992 after earning a Bachelor of Science at Portland State University and a Master of Science degree from the University of Arizona.

Chris Cone is an IC packaging product marketing manager at Siemens EDA with a diverse technical background spanning both design engineering and EDA tools. His unique combination of hands-on design experience and deep knowledge of EDA tools provides him with valuable insights into the challenges and opportunities of modern semiconductor packaging, particularly in automated workflows for FOWLP.

Editor’s Notes

This is third and final part of the article series on 3D IC. The first part provided essential context and practical depth for design engineers working on 3D IC systems. The second part highlighted 3D IC design toolkits and workflows to demonstrate how the integration technology works.

Related Content

• 3D IC Design
• Thermal analysis tool aims to reinvigorate 3D-IC design
• Heterogeneous Integration and the Evolution of IC Packaging
• Tighter Integration Between Process Technologies and Packaging
• Advanced IC Packaging: The Roadmap to 3D IC Semiconductor Scaling

The post Automating FOWLP design: A comprehensive framework for next-generation integration appeared first on EDN.

Whether it be designing products and creative content or software engineering, artificial intelligence is steadily changing how we engineer and interact with technology. But although AI can speed up the development process, the real price of the measure lies in trusting its output, particularly when dealing with safety-critical applications. How can AI-generated software be ensured to be correct, secure, and efficient within real-world parameters?

Bosch Research recognizes the immense promise of the generation-and-execution approach in driving innovation and practical impact. This synthesis combines generative AI to suggest solutions and systematic checks to enforce correctness, safety, and performance. Balancing AI creativity occurs with a touch of strictness-a balance that lands well upon software engineering.

How Generate-and-Check Works

Think of solving a crossword puzzle: you may try out different words, but each suggestion is validated against the length of the clue and the letters already in place. Similarly, in software engineering, AI can generate new code or refactor existing code, while automated checks verify compliance with rules and desired outcomes.

Those rules can be either very simple like the coding style enforcement or highly advanced, like formal verification of software properties. From this perspective, rather than verifying every possible system state, safety, correctness, and adherence to requirements are ensured by verifying AI proposals.

Less error-prone AI assistance, and much less reliance on human supervision all the time.

Use Case 1: Smarter Code Refactoring

Refactoring is a perfect application for generate-and-check. The AI proposes improvements, e.g., migrating to more efficient frameworks, while automated checks verify the equivalence of the new version with the old code.

This approach is somewhat different from the traditional ones based mostly on unit tests as it guarantees behavioral invariance, i.e., that the refactored code behaves exactly the same but better in terms of maintainability or efficiency. Tools developed at Bosch Research allow you to profile this too, to make sure that performance has stayed the same or improved after the changes have been made.

Use Case 2: Reliable Software Translation

On the other hand, software translation remains an area where AI excels but demands human monitoring. The idea of translating legacy code into a safer or new-age language seems nice, but oftentimes traditional transpilers would fail in preserving the idiomatic essence of the target environment.

Yet with generate-and-check, AIs can translate idiomatically while automated tools check for functional correctness, safety, security, and performance. This finally offers a chance to modernize codebases in great bulk without stealthily inserting vulnerabilities.

Embedding into the Developer Workflow

AI becomes valuable for developers if their tools support integration with existing toolchains. Generate-and-check would appear in various forms:

IDE plugins for quick, low-latency assistance during coding.

Background workflows for longer tasks, such as legacy migration, where AI proposals can be rolled out as pull requests. Each PR can provide evidence, such as performance metrics or validation checks, preserving developers’ agency albeit under automated rigor.

This guarantees that AI will continue to be an aid rather than a substitute, offering reliable recommendations while developers make the ultimate choices.

Looking Forward:

The generate-and-check paradigm is a mentality shift for trustworthy AI in software engineering, not merely a technical approach. AI offers safer, better, and more efficient software development by combining its generating capacity with reliable verification.

(This article has been adapted and modified from content on Bosch.)

The post Building Trustworthy Software with AI: The Generate-and-Check Paradigm appeared first on ELE Times.

Here’s a pair of 99.9985 kHz crystals from an HP3571A spectrum analyzer. They were used in a 5-stage filter that set the IF bandwidth, and are simply gold-plated flat quartz plates with centered contacts on both sides, packaged like vacuum tubes. Manufactured by Northern Engineering Laboratories, Burlington WI submitted by /u/10ppb [link] [comments]

This article explores the concept of indirect FM generation and the basic operation of the Armstrong modulator.

Hi everyone!, after 10 months of working and improving on my accelerator, its finally complete! This device accelerates a magnet in circles using 4 electromagnets and hall effect sensors (I've tried IR sensors but failed😔). Those sensors detect the magnet and then a N-MOSFET switches the coil on and off at the right moment, which leads to acceleration of the magnet. I've also used a 12v--> 5v voltage regulator and for one reason or another I've put a quick ignition and fire hazard or whatever you call it on the voltage regulator. If you wanna know more, or just wanna see the accelerator in action you find the youtube video at the KIWIvolt youtube channel. I'm thinking to make a part 2 in which the magnet is a sphere and thinking of replacing the breadboard with a PCB. If you have any other ideas or wishes please let me know so i can adjust it, to perfect my accelerator even further. submitted by /u/Affectionate-Play484 [link] [comments]

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

So i used HEF4094BP, i did the same circuit in this video 4094 shift register long time ago, then in 2022 i bought raspberry pi pico, and in this year i write a long code with MicroPython to count from 1 to 9 and repeat the loop, but i need to optimise it next time. submitted by /u/HichmPoints [link] [comments]

Over the last few weeks I’ve worked on an Arduino board connected through an ADC converter into 3 magnetometers. They are set orthogonally to one another (around the clear box) so that the magnetic field strength and direction at a given point can be found. The whole lot gets power through a USB cable that allows you to model the direction and strength in python. It’s been an absolute blast building it :) submitted by /u/ArticleWonderful2374 [link] [comments]

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Left: 1974 (Matsushita Electric) Right: 2021 (Rubycon) Both 16V 1,000μF. Same voltage rating and capacitance, but shrunk this much in about 50 years. submitted by /u/NEET_FACT0RY [link] [comments]

For a biochemical project of mine I needed a very precise scale. The ones I bought were underwhelming, so I decided to just solder one myself. The sensitivity is kind of ridiculous. Sitting near the scale, I can see my heartbeat in the signal when streamed to a PC. Someone walking on a different floor makes the reading jump — and I live in a concrete building. The coil can lift about 20 g. With different coils, you could trade off dynamic range vs. precision. For my purposes, the precision is already overkill. Components were about $100 total. The most expensive part was the neodymium magnet. The principle is electromagnetic force restoration. A 110 Ω coil suspended on a lever lever sits above a neodymium ring magnet. The lever height is held constant by a feedback loop that uses an IR photointerrupter. The current required to hold the weight is directly proportional to the mass. For current sensing I used a 10 Ω shunt resistor (RJ711, 5 ppm/°C TCR) and a 24-bit ADC (ADS1232). The signal is read by an Arduino Nano and displayed on a small LCD (SLC0801B). The photointerrupter is built from a generic IR LED and IR photodiode. The LED is driven with a constant current source (using a 2N7000 MOSFET), while the photodiode is reverse-biased for fast response. The circuit runs from a low-drift 2.0 V reference (REF5020), which provides a stable reference for the ADC. After dividing it to 0.5 V, it also biases the photodiode stage and provides the ADC’s negative input. The coil current is controlled with an N-channel power MOSFET (IRF540N) acting as a low-side driver, operated in its ohmic region. Its gate is driven by the photointerrupter circuit. Zero-drift op-amps (OPA187) buffer the reference voltages, drive the photointerrupter, and control the coil current. I also added a capacitive touch button for tare, so you don’t have to touch the scale directly — that’s surprisingly important at this sensitivity. The schematic looks a bit op-amp heavy, but it’s actually pretty straightforward. Challenges and possible improvements - The lever tends to oscillate, so the feedback loop has to be very fast. A lighter lever with a higher resonant frequency would help, and might require a lower-gate-capacitance MOSFET. - All components in the feedback path need low temperature coefficients to minimize drift. - To fully eliminate drift, one would need to monitor and compensate for coil temperature, photointerrupter temperature, as well as ambient air temperature, humidity, and pressure (for buoyancy effects). - A parallel guide system will eventually be needed so measurements are independent of where the weight is placed on the lever. This build definitely requires some electronics background, so it’s not a first-project type of thing. But if you’re comfortable with soldering and op-amps, it’s very doable. Hope you like it 🙂 submitted by /u/sir_alahp [link] [comments]

This was a brain fart moment upon finding out they were .25 watt, we needed 9 watt capable. This is a lovely bundle of 36 that has next to no resistance now 🤦 .... 20ohm submitted by /u/No-Release3675 [link] [comments]

Miss the old micro SD upgrade days submitted by /u/Nerfarean [link] [comments]

I've had an obsession with rockets/flight controllers and decided to make an open source flight controller from scratch (nicknamed Athena). I've added the Github repo/design files if anyone wants to take a closer look. 👉Github repo / Design files Features Triple MCU: STM32H753VIT6 (MPU), STM32H743VIT6 (TPU), STM32G474RET6 (SPU) 6 Pyro Channels: Direct 12V battery connection with fuse protection 6 PWM Channels: 2 for TVC (Thrust Vector Control), 4 for fin control Sensors: Triple ICM-45686 IMUs, LIS2MDLTR magnetometer, ICP-20100 & BMP388 barometers GNSS & Communication: NEO-M8U-06B GPS, LoRa RA-02 telemetry, Bluetooth DA14531MOD Storage: SD Card + Winbond W25Q256JV flash memory Power Management: 7.4-12V LiPo battery with BQ25703ARSNR charger, USB-C PD support 6-Layer PCB: Signal/GND/Power/Signal/GND/Signal submitted by /u/MinecraftPhd [link] [comments]

Built for battery-operated devices, the new CMOS op amp enables ultra-efficient signal conditioning in wearables, IoT, and compact sensing applications.

Are you a first year electronics engineering student? Or maybe working on a second or third degree in this field? All About Circuits has the resources you need: comprehensive textbooks, technical articles, calculators, videos, and more for your EE studies.

Solar thermoelectric generators (STEGs) are used for direct conversion of impinging solar and thermal energy into electricity. It can be an alternative to photovoltaic cells in some cases, which can only make use of sunlight. STEGs consist of a hot side and a cold side separated by semiconductor materials, and the temperature difference between them generates electricity through the well-known Seebeck effect, Figure 1.

Figure 1 New, high-efficiency STEGs were engineered with three strategies: black metal technology on the hot side, covering the black metal with a piece of plastic to make a mini-greenhouse, and laser-etched heat sinks on the cold side. Source: University of Rochester / J. Adam Fenster

However, widespread use of STEGs has been limited by their extremely low efficiency, typically under 1 percent; in contrast, standard consumer-grade solar panels have an energy-conversion rate of roughly 20 percent.

A team at the University of Rochester has focused on this low-efficiency challenge, but not by seeking to develop more advanced or esoteric materials. Instead, they used enhanced spectral engineering and thermal management methods in three ways to create a STEG device that generates 15 times more power than previous devices, Figure 2.

Figure 2 Theoretical design of spectral engineering and thermal management strategies for the STEG hot and cold sides: a) Schematic of enhancing STEG output power through hot- and cold-side thermal management. The hot-side thermal management system consists of a W-SSA and a greenhouse chamber to reduce heat loss. The cold-side thermal management system consists of a μ-dissipator, which enhances the cold-side heat dissipation. b) Four cases of STEG with (I) no thermal management, (II) hot-side thermal management, (III) cold-side thermal management, and (IV) both sides thermal management. c) Simulated STEGs’ peak output power with different thermal management strategies. d) Simulated energy flows in the four STEGs. The blue bars represent the energy flow through the STEG. Source: University of Rochester / J. Adam Fenster

By focusing on the hot and cold sides of the device, and by combining better solar energy absorption and heat trapping at the hot side with better heat dissipation at the cold side, they improved efficiency to about 15%.

First, they applied a specialized black metal technology developed in their lab to the hot side of the device, by modifying ordinary tungsten to selectively absorb light at solar wavelengths. They did this by using intense femtosecond laser pulses to etch nanoscale structures into the metal’s surface, which increased its ability to capture energy from sunlight while limiting heat loss at other wavelengths.

Second, the researchers covered the black metal with a piece of plastic to make a mini greenhouse. This minimized the convection and conduction to trap more heat, increasing the temperature on the hot side.

Third, on the cold side of the STEG, they once again used femtosecond laser pulses, but this time on regular aluminum. This created a heat sink with tiny structures that improved the heat dissipation through both radiation and convection, Figure 3. Doing so doubles the cooling performance of a typical aluminum heat dissipator.

Figure 3 A close-up of laser-etched nanostructures on the surface of a solar thermoelectric generator. Source: University of Rochester / J. Adam Fenster

Their tests and analysis separated the three improvement changes they implemented, so they could confirm the impact of each individual enhancement and compare it to their simulations, Figure 4.

Figure 4 Synergistic effect of STEG hot- and cold-side spectral and thermal management: a) Schematics of four cases of STEG with different thermal management strategies. b) STEG weight increases when adding the μ-dissipator, W-SSA, and greenhouse chamber to the TEG. c) STEG power-current curves under 3 suns. d) STEG peak output power under 1–5× solar concentrations. e) STEG power enhancement and TEG average temperatures under 1–5× solar concentrations by applying spectral and thermal management on both sides. f) Photos of LED illumination when powered by the four STEGs in (a). Source: University of Rochester / J. Adam Fenster

It’s obviously not possible to say how successful or practical this STEG approach will be. Nonetheless, it’s interesting to see their focused approach to the weaknesses of STEGs and how they avoided working on the materials-science aspects, but instead concentrated on design improvements. The work is detailed in their paper “15-Fold increase in solar thermoelectric generator performance through femtosecond-laser spectral engineering and thermal management” published in Light: Science & Applications.

Bill Schweber is an EE who has written three textbooks, hundreds of technical articles, opinion columns, and product features.

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The growth of IoT depends on billions of sensor nodes that must operate reliably without frequent maintenance. Powering them through traditional batteries alone is costly and unsustainable, especially when replacements are required at scale. Energy harvesting offers an attractive path toward self-sustaining devices, enabling sensors to draw power from their surrounding environment light, motion, heat, sound, or even radio waves.

Yet, deploying energy harvesting systems is not without hurdles. Limited standards, variable energy sources, and integration complexity raise questions around long-term reliability and return on investment. Selecting the right harvester technology depends heavily on the environment in which the sensor will operate.

Choosing the right energy source

Energy harvesters have infrastructure-specific physical phenomena, and their utility strongly depends on the situation:

• Photovoltaic (PV): Good in the presence of light, including new-generation indoor PV cells capable of producing electricity at just 50 lux.
• Vibration/kinetic: Good where there is some regular motion involved-type actions on the roads, or in machinery environments.
• Thermoelectrics (TEG): How does this generate power? Temperature difference, obviously, which is why it finds use in industries or on the wearer.
• Acoustic: Uses sound waves. Probably better in a noisy industrial setup.
• RF energy harvesting: From Wi-Fi sources, cellular, or even dedicated transmitters, they usually provide very low powers, just enough to wake a device.

Therefore, to power devices capable of some form of heavy industrial application, it is generally necessary to instrument several harvesters acting simultaneously, depending on conditions of daylight, silence, and noise-this, in fact, introduces added complexity to design.

Building blocks of self-sustaining IoT nodes

An energy-harvesting IoT node contains:

1. Harvester/s- for gathering ambient energies.
2. PMIC- to regulate, store, and distribute energy.
3. Energy storage- battery or capacitor to buffer power.
4. Sensor, MCU/SoC and wireless interface- for low-power operation.

The new PMICs have become increasingly versatile now, supporting a variety of harvester types and enabling their dynamic optimization. When complemented with features such as the Maximum Power Point Tracking (MPPT), ultra-low quiescent currents (sub-100 nA), and adaptive duty cycling, the nodes are able to effectively optimize performance with respect to erratic energy input.

Choosing storage depends on the application’s requirements:

• Batteries: High energy density, therefore, good for sustained powering, but lifespan in terms of charge cycles is limited.
• Capacitors (including supercapacitors): They can charge and discharge quickly and have a very long lifecycle, but low energy storage.

Leakage currents, environmental condition, and duty cycle also decide which is the best option. Real testing is a must, as datasheet specifications can never accurately predict real operating environments.

Energy management in action

Moving beyond the hardware, there is a rise in advanced software techniques. Reinforcement learning (RL) allows energy allocation to be optimized by teaching sensor nodes when to send data, when to go into sleep mode, and how to adjust power depending on the energy available. Machine learning merges with the efficiency of hardware to make IoT systems more autonomous, thus improving resilience.

Toward a sustainable IoT ecosystem

Energy harvesting could potentially eliminate its frequent replacement, reduce environmental damages, and thus extend the lifetime of the device. Success lies in an all-encompassing design approach that involves choices such as ultra-low-power components, energy-efficient communication protocols, and adaptive power management capable of handling the variability of real-world conditions.

Any IoT device that is to become truly self-sustaining needs just the right harvesters working along with smart PMICs and optimized storage.

(This article has been adapted and modified from content on Avnet.)

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Bosch Sensortec, which shipped more than 1 billion MEMS sensors in 2024, is now a 20-year-old outfit with an ambitious goal of making MEMS sensing as essential to consumer electronics as the microprocessor.

Market research firm Yole Group has acknowledged Bosch Sensortec as the global market leader in MEMS sensors for the fourth consecutive year. “Bosch Sensortec has been one of the main driving forces in the MEMS industry,” said Jean-Christophe Eloy, president and founder of Yole Group. “The company has evolved from a startup with a strong technical vision into a global leader in intelligent sensing for consumer electronics.”

The timing of Bosch Sensortec’s inception in 2005 was impeccable; the smartphone revolution would arrive a couple of years later, transforming the MEMS sensor world by bringing sensor technology into real-world impact. “As smartphones began to change the world, we brought deep technical expertise,” said Stefan Finkbeiner, CEO of Bosch Sensortec.

He recalls the early days when a handful of engineers would all fit in a single meeting room. “I remember us playing early mobile phone games in that room just to understand how a gyroscope might enhance the user experience.” Over the years, miniaturization became the key driving force by combining MEMS and ASIC layers through advanced packaging technologies such as through-silicon vias and buried bonding.

It reduced the sensor footprint and enabled AI computation directly on the chip. “Twenty years ago, our first MEMS accelerometer was 15 times larger in package volume than today’s ultra-compact sensors,” Finkbeiner said. “Today, you can hardly see these sensors; they’re only slightly bigger than a grain of sand.”

Figure 1 Miniaturization transformed MEMS sensors in the past two decades. Source: Bosch Sensortec

First and foremost, this miniaturization opened the door to new applications in space-constrained environments, spanning from true wireless stereo earbuds and wearables to smart home devices. Moreover, instead of redesigning hardware, design engineers can update software to adapt functionality, speeding up time-to-market and enabling broader use cases.

MEMS sensors in the AI era

The next tectonic shift in the MEMS sensor space is related to artificial intelligence (AI). Bosch Sensortec describes itself as a supplier of intelligent sensing systems that integrate MEMS technology, embedded software and edge AI.

Consumer electronics products—from smartphones and wearables to smart homes and hearables—are connected devices that require more than raw data. They demand context and energy-efficient intelligence. Here, AI-powered sensors that process data directly on the sensor itself ensure privacy, extend battery life, and enable new features like activity recognition, gesture control, and indoor navigation.

Figure 2 The AI-powered sensors transform raw data into actionable signals for smartphones, wearables, hearables, and smart homes. Source: Bosch Sensortec

Bosch Sensortec claims that 90% of its products will feature on-sensor intelligence by 2027. Furthermore, its long-term goal is to deliver over 10 billion intelligent sensors in total by 2030. “From silicon to system, we’re building sensor solutions that shape tomorrow’s connected, sustainable technologies,” Finkbeiner said.

He concludes by saying that while the company’s startup phase may be over, the spirit of experimentation remains. That’s a vital premise for AI‑driven sensor systems in a connected world.

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