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Manufacturing today depends on processes that balance speed, precision, and scalability. Among them, injection molding has become indispensable for industries ranging from healthcare to consumer goods. Its ability to deliver identical, high-quality parts in massive volumes makes it one of the most reliable and cost-effective production methods. But what makes this process so vital, and how exactly does it work?

Understanding Injection Molding

Fundamentally, injection molding is about thrusting molten material into a precisely crafted mold, where it solidifies and takes on its final shape. Plastics are the stalwart of the operation, but producers also apply it to metals and testing uses in new industries. The greatest strength of injection molding is consistency and efficiency once a mold has been made, it can be used to churn out hundreds of thousands of duplicate parts with little deviation.

Unlike subtractive methods such as CNC machining, injection molding is less wasteful of material and can be more flexible in terms of design, with the ability to create everything from small medical devices to large automotive panels.

Industries that Depend on Injection Molding

• Food and Beverage

From yogurt cups to condiment containers, the packaging business relies heavily on injection molding for its light, disposable products. Moving beyond packaging, researchers at one of the University are testing whether this process can be used to mass-produce plant-based meat substitutes, demonstrating how versatile the method can be. In contrast to 3D printing, injection molding offers cost savings and is able to maintain taste and texture in food applications.

• Healthcare and Medical Devices

The medical sector applies injection molding in the production of syringes, implants, and wearables. Due to the stringent regulatory conditions, manufacturers tend to include sensors within the mold to check for temperature and pressure, allowing for perfect outcomes. Robotic equipment is also utilized, which removes faulty components automatically to ensure high levels of safety in patient-care products.

• Sporting Goods and Consumer Products

Leisure goods used daily picnic tableware, coolers, and even high-precision golf clubs are produced with this process as well. Metal injection molding enables golf club manufacturers to create products that improve performance and feedback. Molding single-piece coolers thinner but stronger walls speaks to the process’s efficiency and resilience.

The Injection Molding Process

In any industry and whether small, medium, or large, the injection molding process adheres to a systematic approach:

1. Material Selection – Companies select metals or polymers according to strength, flexibility, durability, or resistance characteristics. Polypropylene is suitable for packaging food, while polycarbonate resists UV exposure for use outside.
2. Design of Mold – Designers make precise steel or aluminum molds with orientation, core, cavity, and mold base in mind. CNC machining is usually employed to cut the mold exactly.
3. Clamping – A clamping mechanism provides pressure to keep the mold halves tightly closed, preventing any leak during the process of injection.
4. Injection – Pellets are melted into molten form, blended by a reciprocating screw, and injected into the mold at regulated velocities and pressures.
5. Dwelling – Pressure is held for a temporary period to guarantee the molten material fills all the cavities of the mold.
6. Cooling – The part solidifies within the mold, a phase often constituting the bulk of cycle time.
7. Opening and Removal – After cooling, the mold is opened and ejector pins force the part out. Any remaining flash material is removed and sometimes recycled.
8. Inspection – Finished parts are visually inspected and tested to detect defects, maintaining consistent quality control.

Why Injection Molding Remains Essential

The scalability, accuracy, and versatility to perform in various industries of the process make injection molding a corner stone of contemporary manufacturing. From life-saving medical technologies to common consumer products, the process continues to transform with automation, robotics, and intelligent sensors, which guarantee ever-greater levels of quality and efficiency.

As industries seek faster, more sustainable, and more innovative ways to produce goods, injection molding remains a cornerstone technology that bridges traditional manufacturing with future possibilities.

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

The post Injection Molding: The Backbone of Modern Mass Production appeared first on ELE Times.

Simple linear and switching voltage regulators with feedback networks of the type shown in Figure 1 are legion. Their output voltages are the reference voltage at the feedback (FB) pin multiplied by 1 + Rf / Rg. Recommended values of Cf from 100 pF to 10nF increase the amount of feedback at higher frequencies, or at least ensure it is not reduced by stray capacitances at the feedback pin.

Figure 1 The configurations of common regulators and their feedback networks. A linear regulator is shown on the left and a switcher on the right.

Modifying this structure to incorporate PWM control of the output voltage requires some thought, and both Stephen Woodward and I have presented several Design Ideas (DIs) that address this.

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

I’ve suggested disconnecting Rg from ground and driving it from a heavily filtered (op-amp-based) PWM signal supplied by a 74xx04-type logic inverter. Although this can result in excellent ripple suppression, it has a disadvantage—the need for an inverter power supply, which does not degrade the accuracy of the regulator’s 1% or better reference voltage.

Stephen has proposed switching the disconnected Rg leg between ground and open with a MOSFET. The beauty of this is that no new reference is needed. Although the output voltage is no longer a linear function of the PWM duty cycle, a simple software-based lookup table renders this a mere inconvenience. (Yup, “we can fix it in software!”)

A general scheme to mitigate PWM controller-induced ripple should be flexible enough to accommodate different regulators, regulator reference voltages, output voltage ranges, and PWM frequencies. In selecting one, here are some possible traps to be aware of:

• Nulling by adding an out-of-phase version of the ripple signal is at the mercy of component tolerances.
• Cheap ceramics, such as the ubiquitous X7R, have DC voltage and temperature-sensitive capacitances. If used, the circuit must tolerate these undesirable traits.
• Schemes which connect capacitors between ground and the feedback pin will reduce loop feedback at higher frequencies. The result could be degradation of line and load transient responses.

With this in mind, consider the circuit of Figure 2, capable of operation from 0.8 V to a little more than 5 V.

Figure 2 A specific instance of a PWM-controlled regulator with ripple suppression. Only a linear regulator is shown, but the adaptation for switcher operation entails only the addition of an inductor and a filter capacitor.

The low capacitance MOSFET has a maximum on-resistance of under 2 Ω at a VGS of 2.5 V or more. Cg1 and Cg2 see maximum DC voltages of 0.8 V (up to 1.25 V in some regulators). Their capacitive accuracies are not critical, and at these low voltages, they barely budge when 10-V or higher-rated X7R capacitors are employed.

Cf can see a significant DC voltage, however. Here, you might get away with an X7R, but a 10-nF (voltage-insensitive) C0G is cheap. The value of Cf was chosen to aid in ripple management. If it were not present, the ripple would be larger and proportional to the value of Rf. With a 10-nF Cf, larger values of Rf for higher output voltages would have no effect on the PWM-induced ripple; smaller ones could only reduce it. The largest peak-to-peak ripple occurs at duty cycles from 30 to 40%.

The filtering supplied by the three capacitors produces a sinusoidal ripple waveform of amplitude 5.7 µV peak-to-peak. For a 16-bit ADC with a full scale of 5 V, the peak-to-peak amplitude is less than 1 LSbit.

You might have a requirement for a wider or narrower range of output voltages. Feel free to modify Rf accordingly without a penalty in ripple amplitude.

Ripple amplitude will scale in proportion to the regulator’s reference voltage. The design assumes a regulator whose optimum FB-to-ground resistance is 10 kΩ. If it’s necessary to change this for the regulator of your choice, scale the three Rg resistors by the same factor Z. Because the resistors and three capacitors implement a 3rd order filter, the ripple will scale in accordance with Z-3. To keep the same ripple amplitude, scale the three capacitors by 1/Z. You might want to scale the capacitors’ values for some other reason, even if the resistors are unchanged.

Changing the PWM frequency by a factor F will change the ripple amplitude by a factor of F-3. But too high a frequency could encounter accuracy problems due to the parasitic capacitances and unequal turn-on/turn-off times of the MOSFET.

Some regulators might not tolerate a Cf of a value large enough to aid in ripple suppression. Usually, these will tolerate a resistor Rcf in series with Cf. In such cases, ripple will be increased by a factor K equal to the square root of ( 1 + Rcf · 2π · fPWM · Cf ), and the waveform might no longer be sinusoidal. But increasing Cg1 and Cg2 by the square root of K will compensate to yield approximately the same suppression as offered by the design with Rcf equal to 0. If all else fails, there is always the possibility of adding an Rg4 and a Cg3 to provide another stage of filtering.

 A flexible approach has been introduced for the suppression of PWM control-induced ripple in linear and switching regulators. Simple rules have been presented for the use and modification of the Figure 2 circuit for operation over different output voltage ranges, PWM frequencies, preferred resistances between ground and the regulator’s feedback pin, and tolerances for moderately large capacitances between the FB pins and the output.

The limitations of capacitors with sensitivities to DC voltages are recognized. These components are used appropriately and judiciously. Dependency on component matching is avoided. Standard feedback network structures are maintained or, at worst, subjected to minor modifications only; specifically, feedback at higher frequencies is not reduced from that recommended by the regulator manufacturer. This maintains the specified line and load transient responses.

Once again, the Comments section of DIs has shown its worth. And it’s Deja vu all over again; value was provided by the redoubtable Stephen Woodward. In an earlier DI, he pointed out that regulators generally do not tolerate negative voltages at their feedback pins. But if there is a capacitor Cf of more than a few hundred picofarads connected from the output to this pin, as I have recommended in this DI, and the output is shorted or rapidly discharged, this capacitor could couple a negative voltage to that pin and damage the part. To protect against this, add the components shown in the following figure.

Figure 3 Add these components to protect the FB pin from output rapid negative voltage changes.

In normal operation and during startup, the CUS10S30 Schottky diode looks like an open circuit and it, Cc, and the 1 MΩ resistor have a negligible effect on circuit operation. Cc prevents the flow of diode reverse current, which could otherwise produce output voltage errors. If Vout transitions to ground rapidly, Cc and the diode prevent any negative voltage from appearing at the junction of the capacitors. Rc provides a cheap “just in case” limit of the current into the FB pin from that voltage transient if it somehow saw a negative voltage. (Check the maximum FB pin current to ensure that no significant error-inducing voltages develop across Rc.) When the circuit has settled, the voltage across Cc is discharged, and the circuit is ready to restart normally.

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

Related Content

• A nice, simple, and reasonably accurate PWM-driven 16-bit DAC
• Brute force mitigation of PWM Vdd and ground “saturation” errors
• A transistor thermostat for DAC voltage references
• Parsing PWM (DAC) performance: Part 1—Mitigating errors
• PWM buck regulator interface generalized design equations

The post Improve PWM controller-induced ripple in voltage regulators appeared first on EDN.

Frequent contributor Christopher Paul recently provided us with a painstakingly conservatively error-budget-analyzed Design Idea (DI) for a state-of-the-art pursuit of a 16-bit-perfection PWM DAC.

The DI presented below, while shamelessly kibitzing on Chris’ excellent design process and product, should in no way be construed as criticism or even a suggested modification. It is neither. It’s just a voyage into the strange land of ultimate precision.

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

In his pursuit of perfect precision, Christopher creatively coped with the limitations of the “art.” Perhaps the most intractable of these limitations in the context of his design was the temperature coefficient of available moderately priced precision voltage references. His choice of the excellent 35xxx family of references, for example, exhibits a temperature coefficient (tempco) of 12 ppm/°C = 0.8 lsb/°C = 55 lsb over 0 to 70°C, reducing this element of conversion precision to only an effective 10.2 bits. 

Since that was more than an order of magnitude worse than other error factors (e.g., DNL, INL, ripple) in Christopher’s simple and elegant (and nice!) design, it got me musing about what possibilities might exist to mediate it. 

Let me candidly admit upfront that my musing was unconstrained by a concern for the practical damage such possibilities might imply towards the simplicity and elegance of the design. This included damage, such as doubling the parts count and vastly increasing the power consumption.

But with those caveats out of the way, here we go.

The obvious possibility that came to mind, of course, was what if we reduced the importance of thermal instability of the reference by the simple (and brute-force) tactic of putting it in a thermostat? Over the years, we’ve seen lots of DIs for using transistors as sensors and heaters (sometimes combining both functions in the same device) for controlling the temperature of single components. Figure 1 illustrates the thermo-mechanics of such a scheme for this application. 

Figure 1 Thermally coupling the transistor sensor/heater to the DAC voltage reference to stabilize its temperature.

A nylon machine screw clamps the heatsink hotspot of a TO-220-packaged transistor (TIP31G) in a cantilever fashion onto the surface of the reference. A foam O-ring provides a modicum of thermal insulation. A dab of thermal grease on the mating surfaces will improve thermal coupling.

Figure 2 shows the electronics of the thermostat. Here’s how that works.

Figure 2 Q1 is a combo heater/sensor for a ±1°C thermostat, nominal setpoint ~70°C. R3 = 37500/(Vref – 0.375).

Q1 is the core of the thermostat. Under the control of gated multivibrator U1, it alternates between a temperature measurement when U1’s “Out” pin is low, and heating when U1’s “Out” pin goes high. Setpoint corresponds to Q1 Vbe = 375 mV as generated by the voltage divider R3/R4, detected by comparator A1, and timed by U1. 

I drew Figure 1 with the R3/R4 divider connected to +5 V, but in practice, this might not be the ideal choice. The thermostat setpoint will change by ~1.6°C per 1% change in Vref, so sub-percentage-point Vref stability is crucial to achieve optimal 16-bit DAC performance. The +5-V supply rail may therefore not be stable enough, and using the thermostatted DAC reference itself would be (much) better.

Any Vref of adequate stability and at least 365 mV may be used by simply setting R3 = 37500/(Vref – 0.375). For the same reason, R3 and R4 should be 1% or better metal film types. The point isn’t setpoint accuracy, which matters little, but stability, which matters much.

Vbe > 375mV indicates Q1 junction temp < setpoint, which gates U1 on. This allows U1 “Out” to transition to +5 V. This turns on driver transistor Q3, supplying ~20 mA to the Q1, Q2 pair. Q2 functions as a basic current regulator, limiting Q1’s heating current to ~0.7 V/1.5 Ω = 470 mA and therefore heating power to 2 W

The feedback loop thus established, Q1 Vbe to A1 to U1 to Q3 to Q1, adjusts the U1 duty cycle from 0 to 95%, and thereby tweaks the heating power to maintain thermostasis. Note that I omitted pinout numbers on A1 to accommodate the possibility that it might be contained in a multifunction chip (e.g., a quad) used elsewhere in the DAC.

Q.E.D. But wait! What are C2 and R2 for? Their reason for being, in general terms, is to be found in “Fixing a fundamental flaw of self-sensing transistor thermostats.”

As “Fixing…” explains, a fundamental limitation on the accuracy of thermostats like Figure 1 is as follows. The junction temperature (Tj) that we can actually measure is only an imperfect approximation of what we’re really interested in: controlling the package temperature (Tc). Figure 3 shows why.

Figure 3 The fatal flaw of Figure 1: the junction temperature is an imperfect approximation of the package temperature.

Because of the nonzero thermal impedance (Rjc) between the transistor junction and the surface of its case, an error term is introduced that’s proportional to that impedance and the heating power:

Terr = Tj – Tc = Rjc*Pj

In the TIP31 datasheet, Rjc is specified in the “Thermal Characteristics” section as 3.125 °C/W. Therefore, as Pj goes from 0 to 2 W, Terr would go from 0 to 6.25 °C. Recalling that the REF35 has a 12 ppm/°C tempco, that would leave us with 12 x 6.25 = 75 ppm = 5 lsb DAC drift. 

That’s 11x better than the 55-lsb tempco error we started with, but it’s still quite a way from true 16-bit accuracy. Can we do even better?

Just like the R11, R12, C2 network in Figure 2 of “Fixing a fundamental flaw of self-sensing transistor thermostats” that adds a Pj proportional Terr correction to the thermostat setpoint, that’s what R2 and C2 do here in this DI. C2 accumulates a ~23 ms average of 0 to 100% heating duty cycle = 0 to 700 mV, and adds through R2 a proportional 0 to 14 mV = 0 to 6.25°C Terr correction to the setpoint for net ±1°C stable thermostasis and < 1 lsb reference instability.

Now Q.E.D!

Stephen Woodward’s relationship with EDN’s DI column goes back quite a long way. Over 100 submissions have been accepted since his first contribution back in 1974.

 Related Content

• Fixing a fundamental flaw of self-sensing transistor thermostats
• A nice, simple, and reasonably accurate PWM-driven 16-bit DAC
• Double up on and ease the filtering requirements for PWMs
• Inherently DC accurate 16-bit PWM TBH DAC
• Self-heated ∆Vbe transistor thermostat needs no calibration
• Take-back-half thermostat uses ∆Vbe transistor sensor
• 1kHz per Kelvin temperature sensor
• Measure junction temperature using the MOSFET body diode on a PG pin

The post A transistor thermostat for DAC voltage references appeared first on EDN.

The world of manufacturing is changing very fast with digital intelligence merging with the conventional industrial processes. Physical AI lies at the heart of this revolution, bringing together sophisticated algorithms and machinery such as robotic arms, autonomous guided vehicles (AGVs), and CNC machinery. For these systems based on AI to function optimally, they depend on real-time information from industrial sensors. Serving as the “eyes and ears” of machines, sensors today do much more than make measurements they allow AI systems to learn, adapt, and optimize processes to enhance productivity, safety, and efficiency.

The two-part series addresses how industrial sensors enable physical AI applications. The first part discusses sensor types and functions in smart factories, while the second part will discuss innovations and trends that will dominate next-generation physical AI-powered industrial systems.

How Industrial Sensors Enable Physical AI

Industrial sensors measure physical parameters like motion, distance, pressure, temperature, or flow into electrical signals that undergo parameterization. These signals find their way into PLCs, CNC machines, and edge AI devices that carry out real-time decision making.

A typical sensor has some or all of these components: sensing element, operational amplifier OpAmp, ADC, processor, interface, and power management. All these or some of them constitute the sensor acting as a bridge between AI algorithms and the physical world, much like the nervous system transmitting information to the brain.

With a modern smart factory, there is an increase in the deployment of AI at the edge, embedding algorithms in sensors, robots, and controllers themselves. This obviates decision making in real-time being made on cloud-based IT systems alone.

Key Industrial Sensor Types

Vision (Image) Sensors: Cameras used to capture product images for machine vision, inspection, and quality control. They recognize orientation, defects, and positioning in real time. Next-generation short-wave infrared (SWIR) and low-power image sensors provide high dynamic range and low-light capabilities in demanding industrial settings.

Position & Torque Sensors: Hall-effect, optical, and inductive sensors are used to detect motor position and torque. Latest inductive PCB-based sensors combine analog front-ends and controllers to make mechanical design easier while providing improved temperature tolerance and contamination resistance.

Ultrasonic Sensors: Detect distance by emitting ultrasonic waves. Suitable for detecting transparent objects, ultrasonic sensors are widely applied in autonomous robots for navigation and obstacle detection and in process automation for flow and level measurement.

Photoelectric Sensors: Capture objects using light-based technologies infrared or laser and come in through-beam, retroreflective, and diffuse-reflective configurations. They are non-contact, flexible, and accommodate long detection ranges.

Proximity Sensors: Sense metallic objects using electromagnetic induction without contact. They are durable in harsh environments and can be used in conjunction with ultrasonic or photoelectric sensors to detect non-metallic objects.

Pressure Sensors: Condition clean-room environments and pneumatic or hydraulic systems. They deliver accurate voltage readings that represent system pressure using strain gauges or force resistors.

Temperature Sensors: Monitor and control temperature in various industries. Thermocouples, RTDs, and semiconductor temperature sensors protect machinery and stabilize processes.

Environmental Sensors: Add gas, chemical, rain, and light sensors to measure environmental conditions and workplace safety. For example, electrochemical sensors can measure chemical currents at low power consumption, providing constant monitoring.

Selecting the Correct Sensors for Intelligent Manufacturing

When designing industrial systems with AI, engineers should keep in mind:

1. Application Response Speed & Accuracy: Response speed and accuracy should be suited to the job, from control of robots to quality inspection in real time.
2. Data Reliability: Sensors need to deliver high-quality data reliably to enable AI learning and analytics.
3. Integration & Interoperability: Sensors need to integrate seamlessly with PLCs, field buses, and other industrial automation.
4. Data Privacy & Cybersecurity: Preserving sensitive operating data is essential, particularly as sensors communicate data through networks.
5. Energy Efficiency: Sensors with low power consumption allow widespread deployment without exceeding power budgets.

Conclusion:

Industrial sensors are critical to enable physical AI in the smart factory spaces. By sensing the physical world accurately and interpreting it, these sensors enable AI systems to make quicker, wiser, and more secure decisions. With advancements in sensor technologies, they will further propel more intelligent, adaptive, and more sustainable industrial activities, leading the way to Industry 5.0.

With its extensive sensor portfolio and application know-how, Onsemi continues to be the leader in intelligent sensing, assisting manufacturers to unlock the full value of physical AI.

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

The post How Industrial Sensors are Powering the Age of Physical AI in Smart Manufacturing appeared first on ELE Times.

A breakthrough in wearable technology is redefining what smart glasses can do: generative AI running entirely on the device, without the need for a phone or cloud connection. Powered by the new Snapdragon AR1+ Gen 1 platform, the glasses allow an AI to interact seamlessly in every day scenarios-from shopping or any home tasks.

AI Fitting Inside Glasses

In a live demonstration, a generative AI assistant operated directly on smart glasses using a compact language model (SLM). During a simulated grocery trip, the assistant helped with a recipe, delivering audio guidance and text directly on the lenses all without any external device. This is a strong demonstration of what is going on with smart glasses from mere accessories to full-blown, standalone AI tools.

Snapdragon AR1+ Gen 1

The Snapdragon AR1+ Gen 1 processor, 26% smaller than previous generations, brings enhanced power efficiency, improved image quality, and the ability to run small language models directly on the glasses. These improvements are crucial for thinner, lighter frames that don’t compromise performance or functionality.

Flexible XR Ecosystem

Next-generation smart glasses will be available in various form factors. Some will be standalone, and others will be linked to nearby devices like smartphones, tablets, or portable computing “pucks.” This modular system provides flexible, high-performance experiences across various configurations while keeping AI interactions speedy, private, and responsive.

Improved Vision and Multimodal Inputs

Sophisticated camera features enable glasses to record and perceive the world in rich detail, enabling proactive suggestion and context-sensitive help. Even when not connected to other devices, these glasses can be paired with other wearables like smartwatches or rings, enabling new forms of interaction and input.

Conclusion

This demonstration represents the beginning of a new era in wearable AI, in which intelligent glasses have the capability to provide tailored, real-time support on the move. Powered by the Snapdragon AR1+ platform, Qualcomm is making some of the thinnest, cleverest, and most powerful glasses possible that might change the way we engage with technology in our everyday lives.

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

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The hardware, software and  Intelligent Factory concept presented by market and technology leader ASMPT drew strong interest from trade visitors at this year’s productronica India.

At the joint booth with long-standing distribution partner Maxim SMT, the spotlight was on the fast, precise, and process-stable DEK TQ solder paste printer platform and the SIPLACE TX high-speed placement solution. The SIPLACE CP20 and SIPLACE CPP placement heads on display also proved particularly well suited to the high-volume production that characterizes the Indian market, offering manufacturers maximum flexibility and productivity in demanding high-volume production.

Integrative concepts for high-volume production

Many visitors took the opportunity to gain a detailed understanding of a complete ASMPT production line in personal technical discussions. Of particular interest was the integrated concept of the intelligent factory, where standardized interfaces across all ASMPT machines continuously collect and process data, making it available where it can be used to enhance quality, prevent errors, and eliminate production bottlenecks.

Comprehensive software portfolio

ASMPT’s extensive software portfolio attracted strong interest from the expert audience. At the core is the WORKS Software Suite, which supports all line-related processes, complemented by the Factory Solutions for holistic optimization across the entire manufacturing environment – including critical areas such as material intralogistics. Live demonstrations featured WORKS Optimization, the intelligent inline expert system for end-to-end process improvement; the Factory Equipment Center, an integrated asset and maintenance management system; the Material Flow Optimizer, ensuring efficient intralogistics and smooth material supply; and SMT Analytics, providing in-depth analysis of the entire SMT production process across all lines.

“We were very pleased with the strong interest shown in our insights and the solutions we showcased for state-of-the-art electronics manufacturing,” summarized Neeraj Bhardwaj, General Manager for India at ASMPT SMT Solutions. “The lively response confirms that we are on the right track in this important growth market.”

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New digital micromirror device with real-time correction enables equipment manufacturers to achieve high-resolution printing at scale, maximizing throughput and yield

What’s new

Texas Instruments is enhancing the next generation of digital lithography with the introduction of the DLP991UUV digital micromirror device (DMD), the company’s highest resolution direct imaging solution to date. With 8.9 million pixels, sub-micron resolution capabilities and a data rate of 110 gigapixels per second, the device eliminates the need for expensive mask technology while delivering the scalability, cost-effectiveness and precision needed for increasingly complex packaging.

Why it matters

Maskless digital lithography machines – which project light for etching circuit designs on materials without a photomask or high-end stencil – are becoming increasingly popular for the manufacturing of advanced packaging. Advanced packaging combines multiple chips and technologies into a single package, enabling high-computing applications, such as data centers and 5G, to have systems that are smaller, faster, and more power-efficient.

With TI DLP technology, system assembly equipment manufacturers can leverage maskless digital lithography to achieve the high-resolution printing at scale necessary for advanced packaging. The new DLP991UUV acts as a programmable photomask, offering precise pixel control with reliable high-speed performance.

“Just as we redefined cinema by enabling the transition from film to digital projection, TI’s DLP technology is once again at the forefront of a major industry shift,” said Jeff Marsh, vice president and general manager of DLP technology at TI. “We’re enabling the creation of maskless digital lithography systems that empower engineers around the world to breakthrough the current limits of advanced packaging and bring powerful computing solutions to market.”

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