For power devices, lead times for MOSFETs, IGBTs, and diodes from onsemi, Infineon, and ST are extending. This is partly driven by substitution demand related to Nexperia parts. In addition, rising costs in 8-inch wafer foundry services and OSAT (assembly & testing) since the beginning of the year are expected to further transmit pricing pressure to finished IC products.

💾 Memory Market: Price increases continue across DDR3/4/5 DRAM, NAND/NOR Flash, eMMC modules, and SSD products. AI-driven demand is intensifying the structural supply-demand imbalance in the memory market. As a result, institutions have revised upward their expectations for memory price growth in Q1.

🔋 Passive Components: Sharp increases in raw material prices such as silver and copper have already triggered multiple supplier price adjustments. In January, Yageo, Walsin, and TA-I announced resistor price hikes. With strong demand from AI and new energy vehicles, MLCC pricing is also moving upward alongside capacity realignment. Key categories including tantalum capacitors, chip resistors, ferrite beads, and MLCCs are now entering an upward cycle.

The overall trend indicates sustained cost pressure across the electronics supply chain in the coming quarters.

We track the biggest discounted part numbers from different franchised distributors

Why do we track the biggest discounted part numbers across franchised distributors worldwide?

It comes down to market dynamics: each franchised distributor supports unique major end customers, creating pockets of concentrated demand. This focus locks in the deepest discounts and gives them stronger “pull-in” power for faster deliveries from the OCM. As a result, different distributors hold unique advantages in different parts.

The challenge? These distributors cannot efficiently collaborate with a widespread, unfamiliar customer base.

Our goal is to move beyond transactional sourcing and establish a stable, formal, and long-term cooperation that delivers sustained value and supply chain reliability.

Looking for datasheets, product specs, or our company profile?

• 📧 Email: frank.cheung@icnets.com
• 🌐 Website: www.icnets.com
• 🏢 Company: ICNets — Connecting the Global IC Networks

Documents available:

• Company Profile (PDF)
• ISO Certificates 9001/1920
• ERAI member
• Product Line Cards
• Quality Control Procedure
• Technical Datasheets

Get in touch directly via WhatsApp or WeChat:

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Disclaimer:

The views and information shared here are solely based on my personal understanding and experience in the electronic components and supply chain industry. This post is intended for informational and educational purposes only and does not constitute any form of commercial, investment, or procurement advice.

Market insights and price observations are derived from public information and personal analysis, which may not be fully comprehensive or up to date. Readers should use their own discretion.

Some materials, charts, and data are sourced from public databases or third-party platforms for non-commercial sharing; all copyrights belong to their respective owners.

The opinions expressed here do not represent the views of my employer or any affiliated organisation.

#SemiconductorMarket #ICMarket #AnalogIC #MemoryMarket #PassiveComponents #AI #SupplyChain #ElectronicsIndustry #DDR #MLCC #MOSFET #IGBT #ChipShortage #MarketTrends

Engineers and designers don’t make these decisions lightly, but when time is tight, it can be difficult to account for every variable. The key is striking a balance between technical performance and practical sourcing.

Let’s explore five critical factors that engineers and designers consider when selecting electronic components.

1. Start with Parametric Search

The component selection process begins with efficient searching. Parametric search tools—like those on ICnets—help you narrow down thousands of options by filtering parts based on detailed specifications and categories.

You can start by entering key product attributes and then refine your list using parameters that align with your design goals. This method quickly reduces the overwhelming pool of parts to a manageable shortlist that meets your basic technical criteria.

2. Validate Specs and Board Compatibility

Once you’ve identified potential parts, the next step is verifying that they meet your technical and physical design requirements. This includes electrical parameters like voltage, current, and frequency, as well as mechanical fit and thermal considerations.

If a component falls short, tools like Supplyframe or ICnets often suggest functional equivalents—saving time and avoiding performance compromises.

3. Check Availability, Pricing, and Lifecycle

A perfect part on paper can still be problematic if it’s out of stock, overpriced, or nearing end-of-life (EOL). Many design delays stem from sourcing issues that weren’t identified early enough.

ICnets offers real-time data on inventory levels, lead times, and pricing from verified distributors. You can also filter by stock status, manufacturer, and quantity. With a free ICnets account, you can set alerts for specific parts to get notified about price changes or availability risks—helping you avoid costly redesigns down the line.

4. Leverage Design Assets: Footprints, Symbols & 3D Models

Datasheets are useful, but design assets bring components to life in your ECAD environment. Footprints, schematic symbols, and 3D models help ensure accurate integration into your board design and reduce errors during layout.

Tools like the Component Search Engine offer free, downloadable assets that integrate directly into popular design platforms—streamlining your workflow and reducing time spent verifying compatibility.

5. Prototype and Test Thoroughly

Even the most thoroughly vetted component needs real-world validation. Prototyping and simulation are essential steps to ensure your design behaves as intended.

Emerging technologies like Digital Twins are revolutionizing this phase. By creating a virtual, high-fidelity model of your product, you can simulate real-world performance and stress scenarios over time—minimizing the risk of failure in the field.

Final Thoughts

Designers need access to accurate, up-to-date tools and intelligence to make informed component choices. Every part on your BOM has unique engineering requirements and sourcing challenges.

With ICnets, you get the real-time data and insight you need to confidently build better products—from prototype to production.

Available Part Numbers:
(Please see the following list of featured components supporting these technologies.)

🧠 Microcontrollers (MCUs)

⚡ Power Management & Voltage Regulators

🔌 Interface & Communication

🔄 Logic & Signal Conditioning

🔧 Discrete Semiconductors

🔋 Diodes & Protection Devices

🧩 Connectors & Cables

🧱 Passive Components

What Is the Humanoid 100?

The Humanoid 100 is Morgan Stanley’s curated list of global companies shaping the humanoid robot ecosystem. From semiconductor giants to industrial component manufacturers and full-fledged robot developers, these firms are at the forefront of a technological revolution. The list isn’t exhaustive but serves as a starting point for investors and enthusiasts eager to explore this rapidly evolving field.

Key takeaways from the report:

• Global Reach, Asian Dominance: 73% of companies involved in humanoids and 77% of integrators (those building complete robots) are based in Asia, with China leading at 56% and 45%, respectively. Western players like Tesla (TSLA) and NVIDIA (NVDA) are prominent, but China’s robust supply chains, local adoption, and government support give it a significant edge.
• Diverse Players: 52% of the listed companies are actively involved in humanoids, while the remaining 48% are poised to enter the market, either as competitors or potential innovators.
• Investor Interest Surge: The topic gained traction after NVIDIA CEO Jensen Huang dedicated 40 minutes to physical AI and robotics at CES 2025, sparking daily inquiries from global investors.

Anatomy of a Humanoid Robot

To understand the value chain, it’s helpful to break down a humanoid robot into its core components:

• The Brain: Powered by semiconductors and software, including generative AI models for autonomy and digital twins for training. These enable robots to think, learn, and adapt.
• The Body: Comprises sensors (e.g., cameras, lidar, force sensors), actuators (motors, bearings, reducers), wiring, and lithium-ion batteries. Lightweight materials like aluminum alloys and plastics form the exterior to optimize mobility.
• The Integrators: Companies like Tesla are assembling these components into fully functional humanoids, bridging the gap between lab prototypes and real-world applications.

For a deeper dive into technical details, the report references the “Anatomy of a Humanoid” section by Morgan Stanley’s China Industrial Analyst, Sheng Zhong, which explores engineering challenges and manufacturing barriers.

Why Humanoids Matter

Humanoid robots represent the next frontier of AI, moving beyond digital interfaces (bits and bytes) to physical embodiments (atoms and photons). This shift could disrupt industries, redefine labor, and create new economic opportunities. The report estimates the TAM at $60 trillion, reflecting the potential to reshape global markets.

However, the ecosystem is still maturing:

• China’s Lead: Startups in China benefit from established supply chains and strong national support, outpacing Western competitors in development speed.
• Western Challenges: Investors note a scarcity of Western firms beyond Tesla and NVIDIA, echoing supply chain struggles seen in the electric vehicle (EV) industry, which shares similarities with humanoids.
• Evolving Landscape: The Humanoid 100 is a snapshot of today’s market, but new entrants and innovations will likely reshape the list in the coming years.

How to Engage with the Humanoid 100

The Humanoid 100 spans three categories:

1. Brain (Semis/Software): Companies developing AI chips and software, critical for robot intelligence.
2. Body (Industrial Components): Manufacturers of sensors, actuators, and materials that form the robot’s physical structure.
3. Integrators: Firms building complete humanoid robots, combining brain and body.

For each company, Morgan Stanley provides details on size, liquidity, core competencies, and their role in the humanoid market. Investors can access the full database through their Morgan Stanley representative to explore specific opportunities.

The Road Ahead

The humanoid robot industry is in its early chapters, with exciting and unpredictable developments on the horizon. Morgan Stanley invites feedback to refine the Humanoid 100, encouraging a collaborative dialogue as the technology evolves. Whether you’re an investor, innovator, or simply curious, the rise of humanoid robots promises to be a defining story of the 21st century.

For more insights, explore Morgan Stanley’s related reports, such as “Humanoid Horizons: Is the ChatGPT Moment Here?” (January 16, 2025) or “Can the US Keep Pace With China?” (October 9, 2024).

Available Part Numbers:
(Please see the following list of featured components.)

Reliable localization and precise navigation are critical for autonomous robots. Whether navigating warehouses, exploring disaster zones, or assisting in surgery, robots need to know where they are and how to reach their destinations accurately. While GPS works well outdoors, it’s often unavailable or unreliable indoors and in complex environments. This is where inertial measurement units (IMUs) come in.

An IMU is a self-contained system that measures a robot’s acceleration and angular velocity. By processing this data, a robot can estimate its position and orientation, providing a crucial complement to other localization methods. This article explores how IMUs enhance robot localization and enable precise navigation, delving into the underlying principles, common challenges, and advanced techniques.

Understanding IMUs

An IMU typically consists of two main components:

• Accelerometer: Measures linear acceleration along three orthogonal axes.
• Gyroscope: Measures angular velocity around three orthogonal axes.

How IMUs Work

1. Data Acquisition: The accelerometer and gyroscope produce raw data in the form of voltage changes proportional to the measured acceleration and angular velocity.
2. Signal Processing: The raw data is filtered to remove noise and calibrated to compensate for sensor biases and scale factor errors.
3. Orientation Estimation: Gyroscope data is integrated over time to determine the robot’s orientation. This process is known as attitude estimation.
4. Position Estimation: Accelerometer data is integrated twice over time to estimate the robot’s position. However, this process is prone to drift errors due to the accumulation of small errors over time.

Enhancing Robot Localization with IMUs

IMUs can enhance robot localization in several ways:

• Dead Reckoning: IMUs enable robots to estimate their position and orientation based on their motion. This is useful for short-term navigation when other localization methods are unavailable.
• Sensor Fusion: IMU data can be combined with data from other sensors, such as cameras, LiDAR, and wheel encoders, to improve localization accuracy and robustness.
• Motion Tracking: IMUs can provide high-frequency motion updates, which are useful for tracking fast movements and sudden changes in direction.
• Indoor Navigation: IMUs can enable robots to navigate indoors and in other environments where GPS is not available.

Sensor Fusion Techniques

• Kalman Filter: A popular algorithm for fusing IMU data with other sensor data. It provides optimal estimates of the robot’s state by weighting the measurements based on their uncertainty.
• Extended Kalman Filter (EKF): An extension of the Kalman filter that can handle non-linear systems, which are common in robotics.
• Unscented Kalman Filter (UKF): Another extension of the Kalman filter that uses a set of carefully chosen sample points to approximate the probability distribution of the robot’s state.
• Graph-Based Optimization: A technique that represents the robot’s trajectory as a graph and optimizes it to minimize the errors between the sensor measurements and the predicted motion.

Challenges and Limitations

IMUs are subject to several challenges and limitations:

• Drift Errors: The most significant challenge with IMUs is drift errors, which accumulate over time due to biases and noise in the sensor measurements. Drift errors can cause the robot’s estimated position and orientation to diverge from its actual state.
• Noise: IMU data is inherently noisy, which can make it difficult to extract accurate information about the robot’s motion.
• Calibration: IMUs require careful calibration to compensate for sensor biases and scale factor errors.
• Computational Cost: Sensor fusion algorithms can be computationally expensive, especially for high-dimensional systems.

Advanced Techniques

Researchers are developing advanced techniques to address the challenges and limitations of IMUs:

• Error Modeling: Developing more accurate models of IMU errors to improve the performance of sensor fusion algorithms.
• Loop Closure: Using vision or other sensors to detect when the robot has returned to a previously visited location, which can be used to correct drift errors.
• Mapping: Building a map of the environment and using it to improve localization accuracy.
• Deep Learning: Using deep learning techniques to learn complex relationships between IMU data and robot motion.

Applications

IMUs are used in a wide range of robotics applications, including:

• Autonomous Vehicles: IMUs are used for navigation, stability control, and sensor fusion in self-driving cars and drones.
• Indoor Navigation: IMUs enable robots to navigate in warehouses, hospitals, and other indoor environments.
• Humanoid Robots: IMUs are used for balance control, gait stabilization, and motion tracking in humanoid robots.
• Surgical Robots: IMUs are used for precise navigation and motion control in surgical robots.
• Virtual Reality: IMUs are used for motion tracking in virtual reality headsets and controllers.

Conclusion

IMUs are essential sensors for enhancing robot localization and achieving precise navigation. By providing high-frequency, self-contained measurements of a robot’s motion, IMUs can complement other localization methods and enable robots to operate in challenging environments. While IMUs have limitations, ongoing research is leading to new techniques that improve their accuracy and robustness. As robots become more prevalent in our daily lives, the role of IMUs in enabling their autonomy will only become more critical.

Available Part Numbers:
(Please see the following list of featured sensor components supporting these technologies.)

Tl ADC10D1500CIUT/NOPB
TI ADC12D1000CIUT/NOPB
Tl ADC12D1600CIUT
TI ADC12D1620CCMPR
TI ADC12D1800RFIUT/NOPB
TI ADC12DJ3200AAV
Tl ADC12DJ5200RFAAV
TI ADC12DL3200ACF
TI ADC12J4000NKE
Tl ADC16DX370RMET
TI ADS5400IPZP
Tl ADS54J60IRMP
Tl ADS54J66IRMP
Tl ADS54J69IRMP
Tl ADS62P49IRGCT
TI DAC37J84IAAVR
TI TMS320C6416TBGLZA8
Tl TMS320C6678ACYPA25
ADI HMC578
ADI HMC625BLP5E
ADI HMC636ST89E
ADI HMC652-SX
ADI HMC659LC5
ADI HMC7044LP10BE
ADI HMC792ALP4E
ADI HMC815BLC5
ADI HMC8191LC4
ADI HMC8191LC4-R5
ADI HMC8205BF10
ADI HMC977LP4
ADI LTC2107IUK#PBF
ADI LTC2165IUK#PBF
ADI LTC2195IUKG#PBF
ADI LTC2207IUK#PBF
Tl TMS320C6746EZWT4
Tl TMS320C6748EZWT4
Tl TMS320F2811PBKA
TI TMS320F2812PGFA
TI TMS320F28335PGFA
Tl TMS320F28335PTPQ
Tl TMS320F28374SZWTT
TI TMS320F28377DPTPQ
Tl TMS320F28377DZWTT
Tl TMS320F28377SPTPQ
TI SM320F2812GHHMEP
TI SMJ320F2812HFGM150
Tl SMJ320F240HFPM40
TI LMD18200-2D/883
MACOM M02015-11
MACOM MA45446-287T
MACOM MAVR-045447-0287AT
MACOM MA4P1250NM-1072T
MACOM MA4P161-134
MACOM MA4P303-134
MACOM MA4P4001F-1091T
MACOM MA4P4006F-1091T
MACOM MA4P504-1072T
MACOM MA4P504-132
MACOM MA4P506-1072T
MACOM MA4PBL027
MACOM MAAL-011078-TR1000
MACOM MAALSS0042
MACOM MAATSS0018
MACOM MAAVSS0006
MACOM MABA-007159-000000
MACOM MABA-007569-ETK42T
MACOM MABA-007748-CT1160
MACOM MADP-000235-10720T
MACOM MADP-000907-14020P
MACOM MADP-011037-13900
MACOM MADR-009443-000100
MACOM MASW-008322
MACOM MASWSS0161
MACOM MASWSS0180
MACOM MAVR0001201411
MACOM MAVR-011020-14110P
MACOM MAVR-045447-0287AT
MACOM MAX3237EAl
MACOM MAX4427ESA
MACOM MEST2G-160-10-CM33
MACOM XP1044-QL
MINI AD3PS-1+
MINI AD4PS-1+
MINI ADP-2-1+
MINI ADP-2-1W+
MINI ADT1-1WT+
MINI ADT2-1T-1P+
MINI BFCN-2275+
MINI BFCN-2975+
MINI BFCN-3115+
MINI EP2C+
MINI EQY-6-63+
MINI ERA-2SM+
MINI ERA-6SM+
MINI GALI-33+
MINI GALI-4+
MINI GALI-74+
MINI GP2X1+
MINI GP2Y1+
MINI GVA-63+
MINI HFCN-2275+
MINI HFCN-3100+
MINI HFCN-3800+
MINI HFCN-5500+
MINI HFCN-740+
MINI HFCN-880+
MINI LFCG-1325+
MINI LFCG-1400+
MINI LFCG-2250+
MINI LFCG-2500+
MINI LFCG-3700+
MINI LFCN-2400+
MINI LFCN-2500+
MINI LFCN-2750+
MINI LFCN-2850+
Wolfspeed CGHV96050F2
Wolfspeed CGHV96100F2
Wolfspeed CMPA2560025F
QORVO QPA9419
QORVO QPA9421
QORVO TGA2239-CP
QORVO TGA2594-HM
QORVO TGA2595
QORVO TGA2595-CP
QORVO TGA2830-SM
QORVO TGA4516
QORVO TQL9092
QORVO QPA9419
ST L4995JTR
ST SPC560B60L5C6E0X
ST STM32F105RBT6
ST STM32F205VGT6
ST STM32F302CCT6
ST STM32F303RCT6
ST STM32F405VGT6
ST STM32F410RBT6
ST STM32F411RET6
ST STM32F427IGT6
ST STM32F427VGT6
ST STM32F427VIT6
ST VN5025AJTR-E
ST VN7020AJTR
ST VND5160AJTR-E
ST VND7140AJTR
ST VNH5050ATR-E
ST VNN3NV04PTR-E
ST VNP10N07-E
ST VNQ5050AKTR-E
ST VNQ5050KTR-E
ST VNQ5160KTR-E
ST VNQ5E250AJTR-E
Microchip ATMEGA1284P-AUR
Microchip ATMEGA328-AUR
Microchip ATMEGA32A-AUR
Microchip ATMEGA644PA-AUR
Microchip ATMEGA64L-8AURA1
Microchip ATMEGA8A-AUR
Microchip PIC10F222T-I/OT
Microchip SST26VF032BT-104I/SM
Microchip SST26VF064BT-104l/SM
MSC JANTX1N4109
MSC JANTX1N4963
MSC JANTX1N4967
MSC JANTX1N5811US
MSC JANTX1N965B-1
MSC JANTX1N967B-1
MSC JANTX1N968B-1
MSC JANTX2N6796
MSC JANTX2N7236
MSC JANTX2N2907AUB
Micron MT25QL01GBBB8E12-0SIT
Micron MT25QL256ABA1EW9-0SIT
Micron MT25QL256ABA8ESF-0SIT
Micron MT29F256G08AUCABH3-10ITZ:A
Micron MT29F2G08ABBGAH4-IT:G
Micron MT40A1G8SA-062F ·R
Micron MT40A512M16TB-062E:R
Micron MT41K128M16JT-125 IT:K
Micron MT41K128M16JT-125:K
Micron MT41K256M16TW-107:P
Micron MT41K512M8DA-107:P
Micron MT48LC8M16A2P-6A IT:L
Micron MT53D512M32D2DS-053 WT:D
Micron MT53E512M32D1ZW-046 WT:B
Micron MT53E768M32D4DT-053 AAT:E
Micron MTA36ASF4G72PZ-2G9E2