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Vibration motors are the silent workhorses behind tactile feedback in wearables and handheld devices. These compact actuators convert electrical signals into physical cues, enriching user interaction. Whether you are prototyping or troubleshooting, understanding their behavior and integration is key to designing responsive, reliable hardware.

Let’s start with the basics: How they generate vibration, and what sets different types apart.

ERM and coin vibration motors

As is often valuable to design engineers, vibration motors can be categorized by form factors to simplify selection and integration. The two primary types are eccentric rotating mass (ERM) vibration motors and coin vibration motors.

ERM vibration motors generate vibration by spinning a mass that is offset from the center of rotation. This off-center mass creates an imbalance, producing the desired vibration effect. These motors typically have a cylindrical form factor, with the rotating shaft and eccentric mass often exposed. Their design is straightforward and well-suited for applications where space constraints are less critical.

Coin vibration motors, sometimes referred to as “pancake” motors, also rely on an offset rotating mass to produce vibration. However, they feature a flat, compact, and fully enclosed form factor. Internally, they contain a short shaft and a flat mass that is offset from the center of rotation, allowing the mechanism to fit within the coin-shaped housing.

Although coin motors operate on the same ERM principle, industry convention typically distinguishes them by form: the exposed cylindrical type is commonly referred to as an ERM vibration motor, while the flat, enclosed type is known as a coin or pancake vibration motor.

Figure 1 ERM and pancake vibration motors generate haptic feedback via eccentric rotating mass. Source: Author

LRA vibration motors

While our primary focus has been on vibration motor form factors, there is another important category worth highlighting: linear resonant actuator (LRA) vibration motors. In terms of external appearance, LRAs often resemble coin vibration motors, sharing the same flat, compact form factor. This visual similarity can be misleading, as the underlying mechanism is fundamentally different.

Unlike ERM motors, which rely on a rotating offset mass driven by a unidirectional current, LRAs operate using a linearly oscillating mass. This mass moves back and forth in a controlled manner, following the principles of simple harmonic motion. Because the direction of movement continuously changes, LRAs require an alternating current (AC) signal with a specific frequency that matches the resonant frequency of the actuator.

This distinction in operating principle allows LRAs to deliver more precise and efficient haptic feedback, making them well-suited for applications where responsiveness and control are critical. Despite their similar form factor to ERM and coin motors, LRAs represent a distinct class of vibration technology.

Figure 2 LRA vibration motor generates haptic feedback via resonant linear actuation. Source: Author

Keep note that there is also a growing category of brushless vibration motors, typically based on brushless DC (BLDC) technology. These motors offer improved durability and efficiency compared to traditional brushed ERM designs, thanks to the absence of mechanical brushes.

While they may share similar cylindrical or coin-like form factors, their internal construction and control requirements differ. Brushless vibration motors are especially useful in high-reliability applications where splendidly long mean time before failure (MTBF) and low maintenance are priorities.

Figure 3 BLDC motors often feature additional wires that enable functions like speed regulation and directional control. Source: Author

How to use actuators

Up next, we take a closer look at how to use these tiny actuators effectively in your designs.

To start with, ERM and coin vibration motors that run on DC can be powered directly from a suitable DC source. But when it comes to haptics—where you want the motor to respond to input—you will probably want to hook it up to a microcontroller. That way, you can control not just the on/off state but also tweak the amplitude and define vibration profiles.

For those seeking integrated driver solutions, ICs such as the NCP5426 offer a reliable and efficient alternative to using a simple BJT or MOSFET.

LRAs, on the other hand, operate on an AC signal and are tuned to a specific resonant frequency. Driving them properly usually means using a dedicated LRA driver to ensure optimal performance.

At this point, it’s worth noting that the DRV2605/DRV2605L from Texas Instruments is a popular motor driver designed for haptic feedback applications. Unlike basic motor drivers, it can generate nuanced vibration patterns, making it ideal for creating tactile feedback that feels responsive and intentional. Thus, it offloads waveform generation from the host processor, simplifying design and saving resources.

Quick note: After reviewing numerous datasheets, a few general trends emerge. Most ERM vibration motors are rated around 3 V, with a starting voltage near 2.5 V and a rated current close to 100 mA at full voltage.

In contrast, most LRAs tend to have a rated voltage of approximately 2 V RMS, a nominal operating current around 150 mA, and a resonant frequency of 150 Hz ±5Hz. That said, consider these figures as ballpark estimates rather than absolutes. Always double-check with the specific datasheet!

Other design considerations

When it comes to mounting vibration motors, they are typically placed within an enclosure or directly onto a PCB. For enclosure-based setups, custom 3D-printed housing can be a convenient way to fasten the motor. If you are mounting the motor to a PCB, many models offer through-hole pins for straightforward soldering. For coin and LRA types, the adhesive backing is usually sufficient for reliable attachment.

As a little extra, here is a handy blueprint for testing/driving a 6-wire vibration motor with integrated driver (Model NFP-BLV3650-FS, for example)

Figure 4 This handy little circuit tests and runs most vibration motors with internal drivers. Source: Author

Just to round things off, there are numerous ways to integrate haptic feedback into your devices, with vibration motors being one of the most accessible options. Whether you opt for a simple implementation or a more sophisticated approach, adding haptics can significantly elevate your device’s user experience and overall effectiveness.

The insights shared here are intended to serve as a springboard, hopefully helping you incorporate haptic feedback into your designs with confidence and creativity.

T. K. Hareendran is a self-taught electronics enthusiast with a strong passion for innovative circuit design and hands-on technology. He develops both experimental and practical electronic projects, documenting and sharing his work to support fellow tinkerers and learners. Beyond the workbench, he dedicates time to technical writing and hardware evaluations to contribute meaningfully to the maker community.

Related Content

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• Motor-driver IC features Hall-element commutation for driving vibration motors

The post Vibration motors: The key to compact haptic solutions appeared first on EDN.

The iconic Etch-a-Sketch, a toy beloved by generations, always had one major limitation: a drawing mistake meant shaking the entire device and starting over from scratch. With the TekkaSketch project, this limitation is overcome thanks to a modern approach that integrates digital functions while preserving the classic aesthetic. The idea stems from observing the original […]

The post TekkaSketch: Reinventing the Etch-a-Sketch with E-Ink and ESP32 Innovation appeared first on Open-Electronics. The author is Boris Landoni

The Ministry of Commerce and Industry has clarified that no additional tariffs have been imposed on Indian exports to the United States in the pharmaceutical and electronics sectors. As per a written reply in the Lok Sabha, this announcement would bring relief to exporters in these sensitive sectors facing concerns about possible duty hikes.

In the meantime, the Ministry said that other goods have been subjected to a reciprocal tariff of 25% from August 7 and that this applies to around 55% in value of India merchandise exports to the US. Furthermore, on August 27, the ad valorem duty of 25% on certain goods will come into being.

The government undertakes consultations with stakeholders, including exporters, MSMEs, and the industry, for the assessment of these measures. It was emphasised that top priority will continue to be given to protecting the interest of farmers, workers, entrepreneurs, and all sections of the industry.

On the trade diplomacy front, India and the US are continuing negotiations on a Bilateral Trade Agreement (BTA) aimed at enhancing market access, reducing tariff and non-tariff barriers, and improving supply chain integration. Talks began in March 2025, with five rounds completed so far the latest held in Washington from July 14 to 18. The US delegation is set to arrive in India by the end of August for the sixth round of Bilateral Trade Agreement negotiation.

The Department of Commerce is closely monitoring the situation to evaluate the potential repercussions of the tariff changes and is working on strategies to mitigate any adverse effects. Measures such as export promotion and market diversification are being explored to support affected industries.

The post Govt Confirms Tariff Stability for Indian Pharma, Electronics appeared first on ELE Times.

In a move to boost India’s electronics manufacturing ecosystem, the Union Cabinet has approved the setup of 4 semiconductor fabrication units at Odisha, Punjab, and Andhra Pradesh.

Having a combined investment of around ₹4,600 crore, these projects will generate direct employment for about 2,034 people and provide a great fillip towards making the country self-reliant in the semiconductor field. This initiative comes under the India Semiconductor Mission (ISM), which is one of the flagship programmes created primarily to lessen the import dependence and create strong manufacturing capabilities within the country.

According to the India Semiconductor Mission, the Union Cabinet approved the setup of four semiconductor manufacturing projects-

Odisha will host two plants in Bhubaneswar’s Info Valley. SicSem Pvt Ltd, in collaboration with UK’s Clas-SiC Wafer Fab Ltd, will set up India’s first commercial compound semiconductor fab to produce 60,000 wafers and 96 million packaged units annually, for use in defence, EVs, railways, chargers, and renewable energy. The second unit, by 3D Glass Solutions Inc., will establish an advanced packaging and embedded glass substrate facility with annual capacity of 69,600 glass panels, 50 million assembled units, and 13,200 3DHI modules for defence, AI, photonics, and high-performance computing.

In Andhra Pradesh, ASIP and South Korea’s APACT Co. Ltd will set up a 96 million unit semiconductor plant for mobile, automotive, and consumer electronics.

In Punjab, CDIL will expand its Mohali facility to produce 158.38 million high-power discrete devices annually, including MOSFETs, IGBTs, and Schottky Bypass Diodes for automotive electronics, EV charging, and industrial use.

According to the Union Minister Ashwini Vaishnaw, these units will cater to both domestic needs and strategic needs, including electronics, telecommunications, defense, and space technologies. Spread across three states, the approved units represent a major infrastructure-building step for semiconductors in India, with a heavy emphasis on job creation, technology innovation, and attracting even more private investment.

Conclusion:

With the approvals, India is decisively charting a course that places it among the global semiconductor firms. Through a mix of value creation through strategic investments, job creation, and technology capacities, the government wants to strategically position itself to meet domestic demand as well as become a deserving assembly hub on the world scale. Industry analysts consider that these projects might be the very foundation that leads toward a self-sustaining semiconductor ecosystem, capable of lessening import dependence and setting India on the pathway of being a key player in future-ready technologies.

The post Union Cabinet Approves Strategic Semiconductor Projects to Strengthen India’s Chip Ecosystem appeared first on ELE Times.

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Kioxia says its UFS version 4.1 embedded flash memory device can improve performance and diagnostic capabilities in automotive and data center applications.

By strategically incorporating artificial intelligence throughout their networks, telecom companies can meet demand for better performance, streamlined operations, and improved customer experiences.

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Several Design Ideas (DIs) have employed the subject ICs to implement programmable current sources in an innovative manner [Editor’s note: DIs referenced in “Related Content” below]. Figure 1 shows the idea.

Figure 1 Two independent current sources, one for loads referenced to the more negative voltage, and the other for those to the more positive one. The Isub current sources control the magnitudes of the currents delivered to the loads.

Wow the engineering world with your unique design: Design Ideas Submission Guide

Each of the ICs works by enforcing Vref = 1.25 V (±50 mV over load current, supply voltage, and operating temperature) between the OUT and ADJ terminals. The Isubs are programmable current sources (PWM-implemented or otherwise) which produce voltage drops Vsubs across the Rsubs.

Given that there are ADJ terminal currents IADJ ( typically 50 µA and maxing out at 100 µA ), the load currents can be seen to be:

[Vref  + ( IADJ – Isub ) · Rsub] / Rsns

When Isub is 0, the load current is at its maximum, Imax, and its uncertainty is a mere ±50 mV / 1250 mV = ±4%. But when Isub rises to yield a desired current of Imax/10, the uncertainty rises to ±40%; the intended fraction of 1.25 V is subtracted, but the unknown portion of the ±50 mV remains. If Imax/25 is desired, the actual load current could be anywhere from 0 to twice that value. Things are actually slightly worse, since the uncertainty in IADJ is a not-insignificant portion of the typically few-milliamp maximum value of Isub.

Circumnavigating the accuracy limitations of reference voltages

Despite the modest accuracy of their reference voltages, these ICs have the advantage of built-in over-temperature limiting. So it’s desirable to find a way around their accuracy limitations. Figure 2 shows just such a method.

Figure 2 Two independent current regulators. The Isub magnitudes are programmable and are often implemented with PWMs. Diodes connected to the ADJ terminals protect the LM ICs during startup. The 0.1-µF supply decoupling capacitors for U1 and U3 are not shown.

The idea of the three-diode string was borrowed from the referenced DIs [Editor’s note: in “Related Content” below]. It ensures that even for the lowest load currents (the LM ICs’ minimum operating is spec’d at 10 mA max.), the ADJ terminal voltages needn’t be beyond the supply rails.

The OPA186 op-amp’s input operating range extends beyond both supply rails (a maximum of 24 V between them is recommended), and its outputs swing to within 120 mV of the rails for loads of less than 1 mA.

The maximum input offset voltage, including temperature drift and supply voltage variations, is less than ±20 µV. An input current of less than ±5 nA maximum means that for Rsubs of 1 kΩ or less, the total input offset voltage is 2000 times better than the LMs’ ±50 mV.

Placing the LM ICs in this op-amp’s feedback loop improves output current accuracy by a similar factor (but see addendum).

Adapting Jim Williams’ design for a current regulator

Jim Williams of analog design fame published an application note placing the LM317 in an LT1001-based feedback loop to produce a voltage regulator. Nothing prevents the adaptation of this idea to a current regulator. The LT1001’s typical gain-bandwidth (GBW) product is 800 kHz, almost exactly the 750 kHz of the OPA186, so no stability problems are expected. And there were none when the LM317 circuit was bench-tested with an LM358 op amp (GBW typically 700 kHz), which I had handy.

Just as you would with the Figure 1 designs, make sure the LM ICs are heatsinked for intended operation. Enclosing them in a feedback loop won’t help if their over-temperature circuitry kicks in. But under the temperature limit, this circuit increases not only load current accuracy, but also the IN-terminal impedances and the rejection of both the power supply and the LM’s references’ noises.

Note that some of the reduction in reference voltage error can be traded off to reduce power dissipation by making the Rsns resistors small. You can also convert the design to a precision voltage regulator by replacing the three-diode strings with a resistor and moving the load to between the OUT terminal and its Rsns resistor’s supply terminal.

Addendum

There’s a missing term in the equation given for load current. In Figure 2, the unknown and unaccounted-for amount of ADJ terminal current is added to the load current.

Considering that the LMs’ minimum specified operating current (see the LM317 3-Pin Adjustable Regulator datasheet and LMx37 3-Terminal Adjustable Regulators datasheet)—and therefore the minimum current through the load—is 10 mA at 25°C, the ADJ maximum of 100 µA is small potatoes. Still, there might be applications where it would be desirable to account for it. Figure 3 is a possible solution, although I’ve not bench-tested it.

Figure 3 Replacing the ADJ terminal-connected diodes with JFETs preserves startup protection for the LM ICs.

The ‘201 and ‘270 JFETS route the ADJ terminal current through the Rsns resistors where it can be recognized and accounted for as part of the current that passes through the load. Cheaper bipolar transistors (which would reroute almost all IADJ) could be used in place of the JFETS, but that would require an additional diode in series with the three-diode string.

Christopher Paul has worked in various engineering positions in the communications industry for over 40 years.

Related Content

• Cross connect complementary current sources to reduce self-heating error
• A negative current source with PWM input and LM337 output
• PWM-programmed LM317 constant current source

The post Improve the accuracy of programmable LM317 and LM337-based power sources appeared first on EDN.

Very on-budget setup. What do you think I should add next? (I've already saved some space for a fume extractor). submitted by /u/GamingVlogBox [link] [comments]

k-Space Associates Inc of Dexter, MI, USA — which produces thin-film metrology instrumentation and software — says that its new kSA RHEEDSim reflection high-energy electron diffraction (RHEED) simulation software is now available for both labs and classrooms...

ClassOne Technology of Kalispell, MT, USA (which manufactures electroplating and wet-chemical process systems for ≤200mm wafers) is providing its Solstice S8 single-wafer processing system to Applied Optoelectronics Inc (AOI) of Sugar Land, TX, USA, a designer and manufacturer of optical components, modules and equipment for fiber access networks in the Internet data-center, cable TV broadband, fiber-to-the-home (FTTH) and telecom markets. The system will further strengthen AOI’s capabilities for producing optoelectronic components that power high-speed data and communications infrastructure...

Deep learning is a subfield of machine learning that applies multilayered neural networks to simulate brain decision-making. The concept is essentially interchangeably with human learning systems which allow machines to learn from data, thus constituting many AI applications we use today-dotting, speech recognition, image analysis, and natural language processing areas.

Deep Learning History:

Since the 1940s, when Walter Pitts and Warren McCulloch introduced a mathematical model of neural networks inspired by the human brain, the very onset of deep learning can be said to have started. In the 1950s and 60s, with pioneers like Alan Turing and Alexey Ivakhnenko laying the groundwork for neural computation and early network architectures, it proceeded forward. Backpropagation emerged as a concept during the ’80s but became very popular with the availability of large computational prowess and data set in 2000. The dawn of newfound applications truly arose in 2012 when, for instance, AlexNet, a deep convolutional neural network, took image classification to another level by dramatically increasing accuracy. Since then, deep learning has become an ever indomitable force for innovation in computer vision, natural language processing, and autonomous systems.

Types of Deep Learning:

Deep learning can be grouped into various learning approaches, depending on the training of the model and the data being used-

• Supervised deep learning models are trained over labeled datasets, which have all input data paired with the corresponding output data. The model tries to learn to map the input data to the output data so that it can later generalize for unseen data through prediction. Among the popular examples of fulfillment of these tasks are image classification, sentiment analysis, and price or trend prediction.
• Unsupervised deep learning operates over unlabeled data, with the system expected to unearth underlying structures or patterns on its own. It is used in clustering similar data points, reducing the dimensionality of data, or detecting relationships among large-size datasets. Examples are customer segmentation, topic detection, and anomaly detection.
• Semi-supervised deep learning places a small set of labeled data against a large set of unlabeled data, striking a balance between accuracy and efficiency in medicine and fraud detection. Self-supervised deep learning lets models create their own learning labels, opening the two fields of NLP and vision to tasks requiring less manual annotation.
• Reinforcement deep learning is a training methodology for machine-learning models where the agent interacts with an environment, receiving rewards or penalties based on its actions. The aim is to maximize the obtained reward and its performance over time. This learning technique is used to train game-playing AIs such as AlphaGo, autonomous navigation, and robotic manipulation.

Deep learning utilizes the passage of data through an array of artificial neural networks, where each subsequent layer extracts successively more complex features. Such networks learn by adjusting the internal weights via backpropagation so as to minimize prediction errors, which ends up training the model to discern various patterns in the input and finally make recognition decisions with respect to the raw input in the form of images, text, or speech.

Deep Learning Applications:

• Image & Video Recognition: Used in facial recognition, driverless cars, and medical imaging.
• Natural Language Processing (NLP): Used to power chatbots, and virtual assistants like Siri and Alexa, and translate languages.
• Speech Recognition: Used for voice typing, smart assistants, and live transcription.
• Recommendation Systems: Personalizes Netflix, Amazon, and Spotify.
• Healthcare: For disease detection, drug discovery, and predictive diagnosis.
• Finance: Used for fraud detection, assessing risks, and running algorithmic trading operations.
• Autonomous Vehicles: Enable cars to detect objects, navigate roads, and make decisions related to driving.
• Entertainment & Media: Supports video editing, audio generation, and content tagging.
• Security & Surveillance: Supports anomaly detection and crowd monitoring.
• Education: Supports the creation of intelligent-tutoring systems and automated grading.

Key Advantages of Deep Learning:

• Automatic Feature Extraction: There is no need for manual data preprocessing. The programs glean important features from raw data on their own.
• High Accuracy: Works extremely well where organization is difficult, such as image recognition, speech, and language processing.
• Scalability: Can deal with huge datasets, much heterogeneous at that, which include unstructured data like text and images.
• Cross-Domain Flexibility: Offers applications in all sectors, including health care, finance, and autonomous systems.
• Continuous Improvement: Deep learning models get even better with the passage of time and more data-ought to be especially more on GPUs.
• Transfer Learning: These kinds of models can be used for other domains after a little setting up; this minimizes human effort and also time required in model engineering.

Deep Learning Examples:

Deep learning techniques are used in face recognition, autonomous cars, and medical imaging. Chatbots and virtual assistants work through natural language processing, speech-to-text, and voice control; recommendation engines power sites like Netflix and Amazon. In the medical field, it assists in identifying diseases and speeding up the drug-discovery process.

Conclusion:

Deep learning changes industries as it can cater to intricate data. The future seems even more bright because of advances like self-supervised learning, multimodal models, and edge computing, which will enable AI to be more efficient in terms of time, context-aware, and capable of learning with the lightest assistance of humans. Deep learning is now increasingly becoming associated with explanations and ethical concerns, as explainable AI and privacy-preserving techniques grow in emphasis. From tailor-made healthcare to the autonomous system and intelligent communication, deep learning will still do so much to transform our way of interfacing with technology and defining the next age of human handwork.

The post Deep Learning Definition, Types, Examples and Applications appeared first on ELE Times.

Nexperia’s MJPE-series BJTs in CFP15B format offer smaller footprints and strong thermal performance for automotive and industrial designs.

I am using arduino and custom PCBs for control. A 12v vacuum pump, 6v air release Valve, and 2 6v lipo batteries. Almost all of this project is 3d printed with the exception of a couple metal brackets. I made a video of this project if you are interested. submitted by /u/ToBecomeImmortal [link] [comments]

I bought $8, got 2500 pics.. capacitor, Mosfet, led, transformer... is this good price? Unboxing video on my YouTube. You can watching if you're curious https://youtu.be/Ld6hYG9f518 submitted by /u/Time_Double_1213 [link] [comments]