#Parallel SRAM memory

[gry2hade]

https://www.futureelectronics.com/p/semiconductors--memory--RAM--static-ram--asynchronous/cy62167ev30ll-45zxi-infineon-9364399

Volatile memory, Computer memory, battery backed SRAM, ram memory

CY62167EV30 Series 16 Mb (1 M x 16/2 M x 8) 2.2 - 3.6 V 45 ns Static RAM-TSOP-48

[rchar2wis]

NON volatile SRAM, non volatile static ram, parallel SRAM memory

AS6C4008-55SINTR AS6C4008 Series 4-Mbit (512 K x 8) 3 V 55 ns CMOS Static RAM - SOIC-32

[phi2abbs]

https://www.futureelectronics.com/p/semiconductors--memory--RAM--static-ram--asynchronous/cy62167ev30ll-45zxit-infineon-9160615

EEPROMs chips, Non volatile RAM memory, NV RAM memories, ram memory

CY62167EV30 Series 16 Mb (1M x 16/2M x 8) 2.2 - 3.6 V 45 ns Static RAM -TSOP-48

[lggn2tonn]

https://www.futureelectronics.com/p/semiconductors--memory--RAM--nvram--quantum-trap-nvsram/m95m02-drmn6tp-stmicroelectronics-8011669

NV SRAM memory, nv ram, Parallel sram, non volatile memories, nonvolatile sram

M95M02 Series 2 Mb (256 K x 8) 5.5 V Serial SPI Bus EEPROM - SOIC-8

[takeoffprojectsservices]

Best VLSI Projects for Final Year Students

Here are some great VLSI project ideas for final-year students:

1. Image Processing System on FPGA: Algorithm, such as edge detection or image filtering should be performed through the usage of FPGAs for optimal performance.

2. Low-Power SRAM Design: Design and simulate a low-power Static Random Access Memory (SRAM) cell, targetting leakage and dynamic power dissipation.

3. Digital Signal Processor (DSP) Design: Design an example of a DSP that will allow a specific signal to be filtered or, for instance, undergo FFT.

4. Wireless Sensor Network (WSN) Protocol Implementation: Devise a VLSI based sensor node for wireless communication that will support protocols used in data transmission.

5. Reconfigurable Hardware Architecture: It is necessary to elaborate a box which is able to evolve in order to support several applications: in this context, it is possible to try to reconfigure parts of the hardware during the runtime according to the specific needs of the client application.

6. Cryptographic Hardware Accelerator: Propose and design a device for which you could use cryptographic algorithms or primitives including AES or RSA where optimization of both speed optimization and security is important.

7. System-on-Chip (SoC) Design: Selected h/w architects use Verilog or VHDL to design a including microcontroller, memory and other peripherals.

8. Artificial Neural Network (ANN) on FPGA: Devise a mini ANN for image recognition and other related work and optimally use the features of parallel processing provisioned by FPGAs.9. Automated VLSI Testing Tool: Design a testing and validation software system that has reduced time and eliminated errors in conducting tests of VLSI designs (Very Large Scale Integration).

10. Temperature Sensor with Data Logger: It will be a VLSI (Very Large Scale Integration) chip for measuring temperature and recording data, with the capability to display the data on a PC or a mobile connection.

All these project proposals present prospects to learn diverse aspects of VLSI design and implementation in addition to enhancing creativity. Choose one that you are interested in and which you can afford to do!

[jsm2vage]

https://www.futureelectronics.com/p/semiconductors--memory--RAM--static-ram--asynchronous/cy62167ev30ll-45bvit-infineon-7951205

Memory ICs, Parallel SRAM memory, Static random access memory, circuits, chips

CY62167EV30 Series 16 Mb (1M x 16/2M x 8) 2.2 - 3.6 V 45 ns Static RAM -TSOP-48

[govindhtech]

Gaudi 2 Intel Benchmarks GenAI Performance Against NV H100

Gaudi 2 Intel

For GenAI Performance, Intel Gaudi 2 is Still the Only Benchmarked Option Besides NV H100. The results of the industry-standard MLPerf v4.0 benchmark for inference were released by MLCommons. The company’s dedication to bringing “AI Everywhere” with a wide range of competitive solutions is reinforced by Intel’s results for its Intel Gaudi 2 accelerators and 5th Gen Intel Xeon Scalable CPUs with Intel Advanced Matrix Extensions (Intel AMX).

When it comes to generative AI (GenAI) performance, the Gaudi 2 Intel AI accelerator is still the sole benchmarked substitute for the Nvidia H100, offering excellent value for the money. Moreover, Intel continues to be the only manufacturer of server CPUs to provide MLPerf findings.

According to MLPerf Inference v3.1, the average improvement in Intel’s 5th Gen Xeon results over 4th Gen Intel Xeon processors was 1.42x. The Reason It Matters Intel’s MLPerf findings provide clients industry-standard standards to assess AI performance, building on its training and inference performance from prior MLPerf rounds.

Intel Gaudi 2 vs Nvidia NV H100 High-Performance AI Accelerator: This robust GPU is intended for usage in data centres for applications such as real-time deep learning inference, rapid data analytics, high-performance computing (HPC), and data analysis. Leading Performance: The H100 offers up to 7x better performance for HPC workloads than prior versions. LLM, or large language model Amicable: With its specialised Transformer Engine, which is intended to handle large LLMs, conversational AI activities may be performed up to 30 times quicker. Scalability and Security: To handle exascale workloads, the NVIDIA NVLink Switch System connects up to 256 H100 GPUs, and NVIDIA Confidential Computing, a built-in feature, secures data and applications. Intel Gaudi 2 The popular large language models (LLMs) and multimodal models that are covered by the Intel Gaudi software package are still growing. Intel submitted the Gaudi 2 accelerator results for the state-of-the-art models Llama v2-70B and Stable Diffusion XL for MLPerf Inference v4.0.

Hugging Face Text Generation Inference (TGI) has been quite popular, and Gaudi’s Llama work took use of this by using the TGI toolbox, which allows for tensor parallelism and continuous batching, thereby improving the efficiency of real-world LLM scaling. Gaudi 2 yielded 8035.0 and 6287.5 tokens-per-second for offline and server tokens, respectively, for Llama v2-70B. Gaudi 2 yielded 6.26 and 6.25 offline samples per second and server queries per second on Stable Diffusion XL, respectively.

Intel Gaudi 2 specs 7nm process technology Dual matrix multiplication engines (MME) and 24 programmable tensor processor cores (TPC) make up the heterogeneous compute architecture. Memory: 48 MB of SRAM and 96 GB of HBM2E onboard memory Networking: 24 on-chip integrated 100 Gbps Ethernet ports The Gaudi 2 Intel delivers outstanding speed and scalability and is optimised for deep learning training and inference applications. It has a heterogeneous computational architecture with twin matrix multiplication engines and 24 programmable tensor processor cores, and it is based on a 7nm manufacturing technology. The Gaudi 2 can effectively perform a range of deep learning tasks because to its design.

Additionally, the Gaudi 2 Intel has 96GB of HBM2E memory built-in, offering enough of capacity for data access. Moreover, the Gaudi 2 has 24 on-chip 100 Gbps Ethernet connectors, allowing several Gaudi 2 Intel accelerators to communicate at high speeds. Because of this, the Gaudi 2 is a good fit for deep learning clusters of any size.

Intel Gaudi 2 price These findings indicate that the Intel Gaudi 2 is still a competitive price. AI Will Be Everywhere in Intel Vision 2024

They are pleased to announce Intel Vision, which will be held in Phoenix, Arizona, on April 8–9, 2024. their flagship event, Intel Vision, brings together elite leaders in business and technology to discuss the most recent developments in the industry and solutions related to client, edge, data Centre, and cloud innovations.

Sign up now to take part in thought-provoking roundtables, captivating demonstrations, and cutting-edge AI insights with Intel executives and distinguished guests that will help you realise your technological vision.e/performance, a crucial factor to take into account when examining the total cost of ownership (TCO).

Concerning the Intel 5th Generation Xeon Outcomes: With advancements in both hardware and software, Intel’s 5th generation Xeon processors outperformed 4th generation Intel Xeon processors in MLPerf Inference v3.1, with a geomean increase of 1.42x. For instance, the 5th Gen Xeon entry demonstrated around 1.8x performance increases over the v3.1 submission for GPT-J with software optimisations including continuous batching. In a similar vein, MergedEmbeddingBag and further Intel AMX optimisations allowed DLRMv2 to provide around 1.8x speed increases with 99.9 accuracy.

Intel takes great pride in working together with OEM partners, like Quanta, Supermicro, Cisco, Dell, and WiWynn, to enable them to submit their own MLPerf results. Additionally, beginning in 2020, Intel has provided MLPerf results for four generations of Xeon CPUs; in many of these submissions, Xeon serves as the host CPU.

How to Utilise Intel Developer Cloud AI Solutions: The Intel Developer Cloud offers evaluations of 5th generation Xeon CPUs and Intel Gaudi 2 accelerators. Users may manage AI computing resources, execute training and inference production workloads at scale (LLM or GenAI), and do much more in this environment.

What to Watch For: Stay tuned for an update on Intel Gaudi 3 AI accelerators and additional information about Intel’s plan to deliver “AI Everywhere” at Intel Vision 2024.

Read more on Govindhtech.com

[lanshengic]

NXP and Xiaomi Vela jointly build an IoT ecosystem to provide a powerful technology engine for the IoT development community

【Lansheng Technology News】Recently, at the 2023 Xiaomi IoT Ecological Partner Conference, NXP Semiconductors was invited to attend as an important partner of Xiaomi and demonstrated the powerful technical resource support provided to the Xiaomi Vela ecological community.

Xiaomi Vela is an embedded IoT software platform built on the open source real-time operating system NuttX and customized for consumer-grade IoT. It can provide a unified software platform on various IoT hardware, shielding the differences in underlying hardware. Through rich components and standardized software frameworks, it provides a unified software interface for upper-level device developers to open up fragmented things. Networked application scenarios greatly reduce the complexity of development and improve development efficiency.

As a global ecological partner of Xiaomi Vela, NXP is deeply involved in the construction of the ecosystem of this development community and has launched a series of technical resources. At this event, NXP highlighted the i.MX RT1060 EVK development kit.

NXP's i.MX RT1060 cross-border MCU is based on the 600MHz Arm Cortex-M7 core and has 1MB on-chip SRAM. It has strong real-time performance and high integration, and is suitable for various industrial and IoT applications. The i.MX RT1060 series provides 2D graphics, camera and various memory interfaces, as well as a wide range of connection interfaces, including UART, SPI, I2C, USB, 2 10/100M Ethernet interfaces and 3 CAN interfaces. Its other features for real-time applications include: high-speed GPIO, CAN-FD, and synchronous parallel NAND/NOR/PSRAM controller.

In addition, i.MX RT1060 has a 2D hardware graphics acceleration PXP module, 3 I2S interfaces for high-performance multi-channel audio, and supports LCD display controller (up to WXGA 1366 x 768). The i.MX RT1060 is available in 225BGA and 196BGA packages, providing greater flexibility with an extended temperature range of -40°C to 125°C.

The i.MX RT1060 series can be developed using NXP's official MCUXpresso tool chain, including SDK, IDE options, and security configuration and configuration tools, enabling rapid development and supporting various real-time operating systems (RTOS) such as FreeRTOS, Xiaomi Vela, Nuttx, Zephyr, etc. .

Xiaomi Vela is fully scalable from micro (8-bit) to mid-range embedded (64-bit) systems with a high degree of standards compliance, easy to port, fully open, highly real-time and powerful. i.MX RT1060 fully supports Xiaomi Vela. Currently supported drivers include ADC, CAN, eLCDIF, ENC, ENET, GPIO, I2S, PWM, SPI, UART and USB. It also supports Vela’s LVGL Demo and can be adapted to Xiaomi The upper component of Vela framework. This combination of soft and hard will provide a powerful technical engine for the development of the Xiaomi Vela ecological community.

Lansheng Technology Limited, which is a spot stock distributor of many well-known brands, we have price advantage of the first-hand spot channel, and have technical supports.

Our main brands: STMicroelectronics, Toshiba, Microchip, Vishay, Marvell, ON Semiconductor, AOS, DIODES, Murata, Samsung, Hyundai/Hynix, Xilinx, Micron, Infinone, Texas Instruments, ADI, Maxim Integrated, NXP, etc

To learn more about our products, services, and capabilities, please visit our website at http://www.lanshengic.com

[micwl2ker]

A complete selection of non-volatile SRAM chips that can be used to design circuits that require non-volatile RAM memories (NV RAM memories) or a parallel SRAM memory

[flatairasd-blog]

Completely independently developed display controller

Brief introduction of PCM-3499 motherboard PCM-3499 is an embedded control module with extremely high cost performance, size and compactness. Almost all the functions required by industrial computers are realized on the PC104 specification board. PCM-3499 onboard embedded high-performance 16-bit processor, which is internally 32-bit RISC architecture and compatible with 80C186 processor, has extremely high performance, main frequency up to 100MHz, built-in 100M Ethernet, and supports 1MB of SDRAM. The instructions are compatible with other X86 microprocessors.

Onboard functions include 10 / 100M high-speed Ethernet interface, PC104 interface, TFT interface and LVDS interface, VGA interface, support standard IDE interface (DOM, ordinary hard disk), parallel port, four serial ports (RS-232 and RS-485, four Each serial port can be configured as TTL level interface), USB interface, DOC interface, PS / 2 keyboard port, RTC real-time clock, ferroelectric, battery-backed SRAM non-volatile memory, watchdog, buzzer interface, general purpose GPIO, 8/16 bit compatible ISA bus, etc. Integrates the system BIOS, 1.44M flash disk

(Flash Flopy Disk-FFD, which can be expanded to 8M flash memory according to user needs) and a 16-point matrix Chinese character library on a single flash memory chip to achieve the smallest size with a minimum number of chips The most functions of the module. Completely independently developed display controller, can achieve up to 1024 768 true color high-performance display effect. If you need more other functions, you can expand the relevant function modules through the PC / 104 bus. Second, PCM-3499 textile controller solution system architecture

In the case of the loom reed suppliers controller successfully developed by Lanyu Technology customers, we briefly introduce one of the customers' textile controller system solutions based on Lanyu Technology PCM-3499. The system architecture is shown in Figure: According to the control realization principle of the entire system scheme shown in the above figure, we divide the main control unit of this scheme into upper and lower computer structures, and PCM-3499 completes the coordination with the upper computer and the control of the terminal equipment . The host computer is mainly responsible for human-computer interaction,

[songsbanana]

Intel r q35 express chipset family pixel shader

#Intel r q35 express chipset family pixel shader for free

#Intel r q35 express chipset family pixel shader zip file

#Intel r q35 express chipset family pixel shader serial

#Intel r q35 express chipset family pixel shader drivers

#Intel r q35 express chipset family pixel shader driver

Ivy Bridge CPUs provide 16 PCIe 3.0 lanes for direct GPU connection and additional 4 PCIe 2.0 lanes. The Z68 also added support for transparently caching hard disk data on to solid-state drives (up to 64 GB), a technology called Smart Response Technology. The Z68 chipset which supports CPU overclocking and use of the integrated graphics does not have this hardware bug, however all other ones with B2 did. Stepping T3 of the Intel 6 collection chipsets will possess the fix for this. Normally these chipsets do not allow unbuffered ECC features. With either a Core we5 or i3 processor chip, the 3400-collection chipsets allow the ECC efficiency of unbuffered ECC storage. Take note that VT-d can be a chipset Memory Controller Hub technology, not a processor function, but this is definitely challenging by later processor ages (Core we3i5i7) shifting the MCH fróm the motherboard tó the processor chip package, making only specific I series CPUs support VT-d.įor high-énd Nehalem processors, thé Back button58 IOH serves as a bridgé from the QPl to PCI Express peripherals and DMI to the ICH10 southbridge.įor mainstream and lower-énd Nehalem processors, thé included memory controller (IMC) is usually an entire northbridge (some also getting GPUs), and the PCH (Platform Controller Centre) acts as a southbridge. VT-d can be broken or non éxistent on some planks until the BIOS is definitely updated. Unofficially, third-párty motherboards (Asus, Gigabyté) support certain 1333FSB 45 nm Primary2 processors, generally with later on BIOS improvements. Usually it and the lower slot (both connected to the Storage Controller Hub) operate at 8 electrically. It facilitates a 1333 MTs FSB with Core 2 Duo processors, but Core 2 Quad processors are usually only backed up to 1066 MTs.Īlso facilitates Hardware Virtualization Technology and Intel Trusted Platform Component 1.2 function. However, some motherboards nevertheless support the older processors.Īssistance for all NetBurst based processors is decreased with this chipset. Support for all NetBurst based processors had been officially lowered starting with the Bearlake chipset household. No support for exterior graphics cards (some planks, like Asus P5GZ-MX, assistance through ICH7 ón PCIe 16 4 lanes setting). Helps another PAT-like setting and ECC memory, and solely uses DDR-II RAM. This primary contains Pixel Shader edition 2.0 only, it does not consist of Vertex Shaders nor will it feature Transform Light (TL) capabilities and thus is not really Direct X 8.1 or 9.0 compliant. Normally RAID10 would have required four tough drives). Replaces AGP ánd CSA with PCl Express, and also supports Matrix RAID, a RAID setting developed to allow the utilization of RAID ranges 0 and 1 simultaneously with two tough drives.

#Intel r q35 express chipset family pixel shader serial

It incorporated DMA controller, an interrupt control Photo, serial and parallel ports, and power-management reasoning for the processor chip.ĭ2 caches are usually direct-mapped with SRAM label Ram memory, write-back for 430FA, HX, VX, and Texas. Intel R Q35 Express Chipset Family Serial And Parallel This chipset can end up being used with an 82335 High-integration User interface Device to provide assistance for the Intel 386SBack button. Be aware: This referrals number 4 is certainly on Times79, which will be a Sandy bridge -Y, not really Sandy Bridge, and PCIe 3.0 just is enabled when an lvy Bridge-E Central processing unit or Xeon Y-5 collection is utilized. Sandy Bridge CPUs will supply up to 40 PCIe 3.0 lanes for direct GPU connectivity and extra 4 PCIe 2.0 lanes. Microsoft and Home windows are possibly registered trademarks or trademarks of Microsoft Company in the United Areas andor other countries.

#Intel r q35 express chipset family pixel shader for free

is definitely not responsible in any way for the functionality of or issues triggered by any third-party motorists.Motorists may also be accessible for free of charge directly from manufacturers websites.

#Intel r q35 express chipset family pixel shader drivers

Intel R Q35 Express Chipset Family Drivers Version Completely.

Intel R Q35 Express Chipset Family Serial And Parallel.

Intel (R) Q35 Express Chipset Intel (R) Q963 Express Chipset Intel (R) Q965 Express Chipset Mobile Intel (R) GL960 Express Chipset Mobile Intel (R) GLE960.

#Intel r q35 express chipset family pixel shader driver

Installs the LAN driver version 12.1 for Intel ® Q35 Express Chipset Development Kit Intel ® Graphics Media Accelerator Driver for Windows Vista* 32(zip).

Video: Intel G31/G33/Q33/Q35 Graphics Controller, Driver, Windows 2000,&nbsp.

Intel(R) G33/G31 Express Chipset Family, Intel(R) Q35 Express Chipset Family,.

#Intel r q35 express chipset family pixel shader zip file

This zip file downloads the INF Update Utility version 9. INF Update Utility - Primarily for Intel.

[severetacoartisan]

“Semiconductor Memory IP Market” report provides a detailed analysis of global market size, regional and country-level market size, segmentation, market growth, market share, and competitive landscape.

[shashiemrf]

Top Key Projections on Global Market for FRAM Market 2026

Overview

The FRAM Market is expected to reach USD 343.2 Million by 2025 at a CAGR of 3.78% during the forecast period. Market Research Future (MRFR), in its report, envelops segmentation and drivers to provide a better glimpse of the market in the coming years. Ferroelectric RAM is a non-volatile memory that features higher speed, and endurance to multiple read/write capabilities than other random-access memory (RAM) storage. FRAM components consume less power as compared to other RAM components, and therefore, are used for applications in consumer electronic devices such as personal digital assistants, power meters, handheld phones, smart cards, and security systems.

Competitive Analysis

The key players of the Global FRAM Market are Fujitsu Ltd (Japan), Rohm Co., Ltd (Japan), Texas Instruments (US), and Cypress Semiconductor Corp (US), among others.

In March 2018, Cypress Semiconductor Corp. announced that MikroElektronika (MikroE), an embedded systems retailer, has selected Cypress 4-Mb serial Ferroelectric Random-Access Memory (F-RAM) to be included in its newest click board with a mikroBUS socket. The FRAM 2 click board is a compact, plug-and-play solution designed to evaluate the data logging and processing capabilities of Cypress F-RAMs, and the board enables faster prototyping and development of the Industrial Internet of Things (IoT) applications.

In December 2017, LAPIS Semiconductor launched a 1Mbit FRAM designed for applications such as smart meters, measurement equipment, medical equipment, and financial terminals that require fast and frequent retrieval of log data and/or fast data backup in emergency situations.

Get Free Sample Report @ https://www.marketresearchfuture.com/sample_request/8518

Segmental Analysis

The Global FRAM Market has been segmented based on product type, interface, application, and region.

Based on the interface, the market has been classified into serial and parallel. The serial segment accounted for the larger market share in 2018, with the higher market value. The serial FRAM features a variety of interface and density options, including serial peripheral interface (SPI) and I2C interface, industry-standard packages, and densities ranging from 4KB to 4MB. Parallel FRAM can be used as a substitute to SRAM in industrial machinery, office equipment, medical devices, and other equipment that currently use SRAM, as it stores data without the need for a battery.

Based on application, the market has been classified into metering/measurement, enterprise storage, automotive, factory automation, telecommunication, medical, others. The telecommunication segment accounted for the largest market share in 2018, with the highest market value. The automotive segment is expected to register the highest CAGR during the forecast period. FRAM is considered for such applications because of its high endurance capability which supports frequent data collection and its ability to instantly capture data on power loss, without any loss of data. In enterprises, non-volatile memories are used to store and retain data. Automotive navigation systems and entertainment systems are often integrated into one infotainment system that allows both systems to access the vehicle’s audio system and display. FRAM is highly reliable and is used mainly for industrial applications, in factory automation equipment, measuring equipment, power meters, and bank terminals, among others. The telecommunication subsystem encompasses modems, transceivers, radio-frequency components, and antennas to communicate with ground stations. The manufacturers of CT scanner have started using FRAM for different purposes, one of which is a control system for determining when the equipment requires maintenance. Apart from the above-mentioned applications, FRAM has applications in smart cards, RFID, security, industrial systems, and other equipment.

Table of Content:

6 GLOBAL FRAM MARKET, BY PRODUCT TYPE

6.1 OVERVIEW 36

6.1.1 4K 36

6.1.2 8K 36

6.1.3 16K 36

6.1.4 32K 36

6.1.5 64K 36

6.1.6 128K 37

6.1.7 256K 37

6.1.8 512K 37

6.1.9 OTHERS (1M, 2M,4M) 37

6.2 MARKET ESTIMATES AND FORECAST, BY PRODUCT TYPE, 2020-2027 37

6.3 MARKET SHIPMENT, ESTIMATES AND FORECAST, BY PRODUCT TYPE, 2020-2027 (MILLION UNITS) 38

6.4 AVERAGE SELLING PRICE, ESTIMATES AND FORECAST, BY PRODUCT TYPE, 2020-2027 (USD) 39

7 GLOBAL FRAM MARKET, BY INTERFACE

7.1 OVERVIEW 40

7.1.1 SERIAL 40

7.1.2 PARALLEL 40

7.2 MARKET ESTIMATES AND FORECAST, BY INTERFACE, 2020-2027 40

7.3 AVERAGE SELLING PRICE, ESTIMATES AND FORECAST, BY INTERFACE, 2020-2027 (USD) 41

8 GLOBAL FRAM MARKET, BY APPLICATION

8.1 OVERVIEW 42

8.1.1 METERING/MEASUREMENT 42

8.1.2 ENTERPRISE STORAGE 42

8.1.3 AUTOMOTIVE 42

8.1.4 FACTORY AUTOMATION 42

8.1.5 TELECOMMUNICATION 43

8.1.6 MEDICAL 43

8.1.7 OTHERS 43

8.2 MARKET ESTIMATES AND FORECAST, BY APPLICATION, 2020-2027 43

9 GLOBAL FRAM MARKET, BY REGION

9.1 MARKET ESTIMATES AND FORECAST, BY REGION, 2020-2027 45

9.2 GLOBAL FRAM MARKET, SHIPMENT, BY REGION, 2020-2027 (MILLION UNITS) 46

9.3 GLOBAL FRAM MARKET, ASP, BY REGION 46

9.4 NORTH AMERICA 47

9.4.1 MARKET ESTIMATES AND FORECAST, BY COUNTRY, 2020-2027 47

9.4.2 MARKET ESTIMATES AND FORECAST, BY PRODUCT TYPE, 2020-2027 48

9.4.3 MARKET ESTIMATES AND FORECAST, BY INTERFACE, 2020-2027 49

9.4.4 MARKET ESTIMATES AND FORECAST, BY APPLICATION, 2020-2027 50

9.4.5 US 51

13.2.5.1 MARKET ESTIMATES AND FORECAST, BY PRODUCT TYPE, 2020-2027 51

9.4.5.1 MARKET ESTIMATES AND FORECAST, BY INTERFACE, 2020-2027 51

9.4.5.2 MARKET ESTIMATES AND FORECAST, BY APPLICATION, 2020-2027 52

9.4.6 CANADA 53

9.4.6.1 MARKET ESTIMATES AND FORECAST, BY PRODUCT TYPE, 2020-2027 53

9.4.6.2 MARKET ESTIMATES AND FORECAST, BY INTERFACE, 2020-2027 53

9.4.6.3 MARKET ESTIMATES AND FORECAST, BY APPLICATION, 2020-2027 54

9.4.7 MEXICO 55

9.4.7.1 MARKET ESTIMATES AND FORECAST, BY PRODUCT TYPE, 2020-2027 55

9.4.7.2 MARKET ESTIMATES AND FORECAST, BY INTERFACE, 2020-2027 55

9.4.7.3 MARKET ESTIMATES AND FORECAST, BY APPLICATION, 2020-2027 56

9.5 EUROPE 57

9.5.1 MARKET ESTIMATES AND FORECAST, BY COUNTRY, 2020-2027 57

9.5.2 MARKET ESTIMATES AND FORECAST, BY PRODUCT TYPE, 2020-2027 58

9.5.3 MARKET ESTIMATES AND FORECAST, BY INTERFACE, 2020-2027 59

9.5.4 MARKET ESTIMATES AND FORECAST, BY APPLICATION, 2020-2027 60

9.5.5 UK 61

9.5.5.1 MARKET ESTIMATES AND FORECAST, BY PRODUCT TYPE, 2020-2027 61

9.5.5.2 MARKET ESTIMATES AND FORECAST, BY INTERFACE, 2020-2027 61

9.5.5.3 MARKET ESTIMATES AND FORECAST, BY APPLICATION, 2020-2027 62

9.5.6 GERMANY 62

9.5.6.1 MARKET ESTIMATES AND FORECAST, BY PRODUCT TYPE, 2020-2027 62

9.5.6.2 MARKET ESTIMATES AND FORECAST, BY INTERFACE, 2020-2027 63

9.5.6.3 MARKET ESTIMATES AND FORECAST, BY APPLICATION, 2020-2027 63

9.5.7 FRANCE 64

9.5.7.1 MARKET ESTIMATES AND FORECAST, BY PRODUCT TYPE, 2020-2027 64

9.5.7.2 MARKET ESTIMATES AND FORECAST, BY INTERFACE, 2020-2027 64

9.5.7.3 MARKET ESTIMATES AND FORECAST, BY APPLICATION, 2020-2027 65

9.5.8 ITALY 65

9.5.8.1 MARKET ESTIMATES AND FORECAST, BY PRODUCT TYPE, 2020-2027 65

9.5.8.2 MARKET ESTIMATES AND FORECAST, BY INTERFACE, 2020-2027 66

9.5.8.3 MARKET ESTIMATES AND FORECAST, BY APPLICATION, 2020-2027 66

9.5.9 REST OF EUROPE 67

9.5.9.1 MARKET ESTIMATES AND FORECAST, BY PRODUCT TYPE, 2020-2027 67

9.5.9.2 MARKET ESTIMATES AND FORECAST, BY INTERFACE, 2020-2027 67

9.5.9.3 MARKET ESTIMATES AND FORECAST, BY APPLICATION, 2020-2027 68

9.6 ASIA-PACIFIC 69

9.6.1 MARKET ESTIMATES AND FORECAST, BY COUNTRY, 2020-2027 69

9.6.2 MARKET ESTIMATES AND FORECAST, BY PRODUCT TYPE, 2020-2027 70

9.6.3 MARKET ESTIMATES AND FORECAST, BY INTERFACE, 2020-2027 71

9.6.4 MARKET ESTIMATES AND FORECAST, BY APPLICATION, 2020-2027 72

9.6.5 CHINA 73

9.6.5.1 MARKET ESTIMATES AND FORECAST, BY PRODUCT TYPE, 2020-2027 73

9.6.5.2 MARKET ESTIMATES AND FORECAST, BY INTERFACE, 2020-2027 74

9.6.5.3 MARKET ESTIMATES AND FORECAST, BY APPLICATION, 2020-2027 74

9.6.6 JAPAN 75

9.6.6.1 MARKET ESTIMATES AND FORECAST, BY PRODUCT TYPE, 2020-2027 75

9.6.6.2 MARKET ESTIMATES AND FORECAST, BY INTERFACE, 2020-2027 75

9.6.6.3 MARKET ESTIMATES AND FORECAST, BY APPLICATION, 2020-2027 76

9.6.7 INDIA 76

9.6.7.1 MARKET ESTIMATES AND FORECAST, BY PRODUCT TYPE, 2020-2027 76

9.6.7.2 MARKET ESTIMATES AND FORECAST, BY INTERFACE, 2020-2027 77

9.6.7.3 MARKET ESTIMATES AND FORECAST, BY APPLICATION, 2020-2027 77

9.6.8 SOUTH KOREA 78

9.6.8.1 MARKET ESTIMATES AND FORECAST, BY PRODUCT TYPE, 2020-2027 78

9.6.8.2 MARKET ESTIMATES AND FORECAST, BY INTERFACE, 2020-2027 78

9.6.8.3 MARKET ESTIMATES AND FORECAST, BY APPLICATION, 2020-2027 79

9.6.9 REST OF ASIA-PACIFIC 79

9.6.9.1 MARKET ESTIMATES AND FORECAST, BY PRODUCT TYPE, 2020-2027 79

9.6.9.2 MARKET ESTIMATES AND FORECAST, BY INTERFACE, 2020-2027 80

9.6.9.3 MARKET ESTIMATES AND FORECAST, BY APPLICATION, 2020-2027 80

9.7 MIDDLE EAST AND AFRICA 81

9.7.1 MARKET ESTIMATES AND FORECAST, BY COUNTRY, 2020-2027 81

9.7.2 MARKET ESTIMATES AND FORECAST, BY PRODUCT TYPE, 2020-2027 82

9.7.3 MARKET ESTIMATES AND FORECAST, BY INTERFACE, 2020-2027 83

9.7.4 MARKET ESTIMATES AND FORECAST, BY APPLICATION, 2020-2027 84

9.7.5 UAE 85

9.7.5.1 MARKET ESTIMATES AND FORECAST, BY PRODUCT TYPE, 2020-2027 85

9.7.5.2 MARKET ESTIMATES AND FORECAST, BY INTERFACE, 2020-2027 85

9.7.5.3 MARKET ESTIMATES AND FORECAST, BY APPLICATION, 2020-2027 86

9.7.6 SOUTH AFRICA 86

9.7.6.1 MARKET ESTIMATES AND FORECAST, BY PRODUCT TYPE, 2020-2027 86

9.7.6.2 MARKET ESTIMATES AND FORECAST, BY INTERFACE, 2020-2027 87

9.7.6.3 MARKET ESTIMATES AND FORECAST, BY APPLICATION, 2020-2027 87

9.7.7 SAUDI ARABIA 88

9.7.7.1 MARKET ESTIMATES AND FORECAST, BY PRODUCT TYPE, 2020-2027 88

9.7.7.2 MARKET ESTIMATES AND FORECAST, BY INTERFACE, 2020-2027 88

9.7.7.3 MARKET ESTIMATES AND FORECAST, BY APPLICATION, 2020-2027 89

9.7.8 REST OF MIDDLE EAST AND AFRICA 89

9.7.8.1 MARKET ESTIMATES AND FORECAST, BY PRODUCT TYPE, 2020-2027 89

9.7.8.2 MARKET ESTIMATES AND FORECAST, BY INTERFACE, 2020-2027 90

9.7.8.3 MARKET ESTIMATES AND FORECAST, BY APPLICATION, 2020-2027 90

9.8 CENTRAL AND SOUTH AMERICA 91

9.8.1 MARKET ESTIMATES AND FORECAST, BY COUNTRY, 2020-2027 91

9.8.2 MARKET ESTIMATES AND FORECAST, BY PRODUCT TYPE, 2020-2027 92

9.8.3 MARKET ESTIMATES AND FORECAST, BY INTERFACE, 2020-2027 93

9.8.4 MARKET ESTIMATES AND FORECAST, BY APPLICATION, 2020-2027 94

9.8.5 BRAZIL 95

9.8.5.1 MARKET ESTIMATES AND FORECAST, BY PRODUCT TYPE, 2020-2027 95

9.8.5.2 MARKET ESTIMATES AND FORECAST, BY INTERFACE, 2020-2027 95

9.8.5.3 MARKET ESTIMATES AND FORECAST, BY APPLICATION, 2020-2027 96

9.8.6 ARGENTINA 96

9.8.6.1 MARKET ESTIMATES AND FORECAST, BY PRODUCT TYPE, 2020-2027 96

9.8.6.2 MARKET ESTIMATES AND FORECAST, BY INTERFACE, 2020-2027 97

9.8.6.3 MARKET ESTIMATES AND FORECAST, BY APPLICATION, 2020-2027 97

9.8.7 COLOMBIA 98

9.8.7.1 MARKET ESTIMATES AND FORECAST, BY PRODUCT TYPE, 2020-2027 98

9.8.7.2 MARKET ESTIMATES AND FORECAST, BY INTERFACE, 2020-2027 98

9.8.7.3 MARKET ESTIMATES AND FORECAST, BY APPLICATION, 2020-2027 99

9.8.8 REST OF CENTRAL AND SOUTH AMERICA 99

9.8.8.1 MARKET ESTIMATES AND FORECAST, BY PRODUCT TYPE, 2020-2027 99

9.8.8.2 MARKET ESTIMATES AND FORECAST, BY INTERFACE, 2020-2027 100

9.8.8.3 MARKET ESTIMATES AND FORECAST, BY APPLICATION, 2020-2027 100

10 COMPETITIVE LANDSCAPE

10.1 COMPETITIVE OVERVIEW 101

10.2 COMPETITIVE BENCHMARKING 102

10.3 KEY DEVELOPMENTS & GROWTH STRATEGIES 103

10.3.1 NEW PRODUCT LAUNCH/SERVICE DEPLOYMENT 103

10.3.2 PARTNERSHIP 104

11 COMPANY PROFILES

11.1 FUJITSU LTD. 106

11.1.1 COMPANY OVERVIEW 106

11.1.2 FINANCIAL OVERVIEW 107

11.1.3 PRODUCTS/SOLUTIONS/SERVICES OFFERED 108

11.1.4 KEY DEVELOPMENTS 109

11.1.5 SWOT ANALYSIS 110

11.1.6 KEY STRATEGIES 110

11.2 ROHM CO., LTD 111

11.2.1 COMPANY OVERVIEW 111

11.2.2 FINANCIAL OVERVIEW 112

11.2.3 PRODUCTS/SOLUTIONS/SERVICES OFFERED 113

11.2.4 KEY DEVELOPMENTS 113

11.2.5 SWOT ANALYSIS 114

11.2.6 KEY STRATEGIES 114

11.3 TEXAS INSTRUMENTS 115

11.3.1 COMPANY OVERVIEW 115

11.3.2 FINANCIAL OVERVIEW 116

11.3.3 PRODUCTS/SOLUTIONS/SERVICES OFFERED 117

11.3.4 KEY DEVELOPMENTS 117

11.3.5 SWOT ANALYSIS 119

11.3.6 KEY STRATEGIES 119

11.4 CYPRESS SEMICONDUCTOR CORP. 120

11.4.1 COMPANY OVERVIEW 120

11.4.2 FINANCIAL OVERVIEW 121

11.4.3 PRODUCTS/SOLUTIONS/SERVICES OFFERED 122

11.4.4 KEY DEVELOPMENTS 123

11.4.5 SWOT ANALYSIS 124

11.4.6 KEY STRATEGIES 124

12 List Of Tables

TABLE 1 GLOBAL FRAM MARKET, BY PRODUCT TYPE, 2020-2027 (USD MILLION) 38

TABLE 2 GLOBAL FRAM MARKET, BY PRODUCT TYPE, SHIPMENT, 2020-2027 (MILLION UNITS) 38

TABLE 3 GLOBAL FRAM MARKET, BY PRODUCT TYPE, ASP, 2020-2027 (USD) 39

TABLE 4 GLOBAL FRAM MARKET, BY INTERFACE, 2020-2027 (USD MILLION) 41

TABLE 5 GLOBAL FRAM MARKET, BY INTERFACE, ASP, 2020-2027 (USD) 41

TABLE 6 GLOBAL FRAM MARKET, BY APPLICATION, 2020-2027 (USD MILLION) 44

TABLE 7 GLOBAL FRAM MARKET, BY REGION, 2020-2027 (USD MILLION) 45

TABLE 8 GLOBAL FRAM MARKET, SHIPMENT, BY REGION, (MILLION UNITS) 46

TABLE 9 GLOBAL FRAM MARKET, ASP, BY REGION, (USD) 46

TABLE 10 NORTH AMERICA: FRAM MARKET, BY COUNTRY, 2020-2027 (USD MILLION) 47

TABLE 11 NORTH AMERICA: FRAM MARKET, BY PRODUCT TYPE, 2020-2027 (USD MILLION) 48

TABLE 12 NORTH AMERICA: FRAM MARKET, BY INTERFACE, 2020-2027 (USD MILLION) 49

TABLE 13 NORTH AMERICA: FRAM MARKET, BY APPLICATION, 2020-2027 (USD MILLION) 50

TABLE 14 US: FRAM MARKET, BY PRODUCT TYPE, 2020-2027 (USD MILLION) 51

TABLE 15 US: FRAM MARKET, BY INTERFACE, 2020-2027 (USD MILLION) 51

TABLE 16 US: FRAM MARKET, BY APPLICATION, 2020-2027 (USD MILLION) 52

TABLE 17 CANADA: FRAM MARKET, BY PRODUCT TYPE, 2020-2027 (USD MILLION) 53

TABLE 18 CANADA: FRAM MARKET, BY INTERFACE, 2020-2027 (USD MILLION) 53

TABLE 19 CANADA: FRAM MARKET, BY APPLICATION, 2020-2027 (USD MILLION) 54

TABLE 20 MEXICO: FRAM MARKET, BY PRODUCT TYPE, 2020-2027 (USD MILLION) 55

TABLE 21 MEXICO: FRAM MARKET, BY INTERFACE, 2020-2027 (USD MILLION) 55

TABLE 22 MEXICO: FRAM MARKET, BY APPLICATION, 2020-2027 (USD MILLION) 56

TABLE 23 EUROPE: FRAM MARKET, BY COUNTRY, 2020-2027 (USD MILLION) 57

TABLE 24 EUROPE: FRAM MARKET, BY PRODUCT TYPE, 2020-2027 (USD MILLION) 58

TABLE 25 EUROPE: FRAM MARKET, BY INTERFACE, 2020-2027 (USD MILLION) 59

TABLE 26 EUROPE: FRAM MARKET, BY APPLICATION, 2020-2027 (USD MILLION) 60

TABLE 27 UK: FRAM MARKET, BY PRODUCT TYPE, 2020-2027 (USD MILLION) 61

TABLE 28 UK: FRAM MARKET, BY INTERFACE, 2020-2027 (USD MILLION) 61

TABLE 29 UK: FRAM MARKET, BY APPLICATION, 2020-2027 (USD MILLION) 62

TABLE 30 GERMANY: FRAM MARKET, BY PRODUCT TYPE, 2020-2027 (USD MILLION) 62

TABLE 31 GERMANY: FRAM MARKET, BY INTERFACE, 2020-2027 (USD MILLION) 63

TABLE 32 GERMANY: FRAM MARKET, BY APPLICATION, 2020-2027 (USD MILLION) 63

TABLE 33 FRANCE: FRAM MARKET, BY PRODUCT TYPE, 2020-2027 (USD MILLION) 64

TABLE 34 FRANCE: FRAM MARKET, BY INTERFACE, 2020-2027 (USD MILLION) 64

TABLE 35 FRANCE: FRAM MARKET, BY APPLICATION, 2020-2027 (USD MILLION) 65

TABLE 36 ITALY: FRAM MARKET, BY PRODUCT TYPE, 2020-2027 (USD MILLION) 65

TABLE 37 ITALY: FRAM MARKET, BY INTERFACE, 2020-2027 (USD MILLION) 66

TABLE 38 ITALY: FRAM MARKET, BY APPLICATION, 2020-2027 (USD MILLION) 66

TABLE 39 REST OF EUROPE: FRAM MARKET, BY PRODUCT TYPE, 2020-2027 (USD MILLION) 67

TABLE 40 REST OF EUROPE: FRAM MARKET, BY INTERFACE, 2020-2027 (USD MILLION) 67

TABLE 41 REST OF EUROPE: FRAM MARKET, BY APPLICATION, 2020-2027 (USD MILLION) 68

TABLE 42 ASIA-PACIFIC: FRAM MARKET, BY COUNTRY, 2020-2027 (USD MILLION) 70

TABLE 43 ASIA-PACIFIC: FRAM MARKET, BY PRODUCT TYPE, 2020-2027 (USD MILLION) 71

TABLE 44 ASIA-PACIFIC: FRAM MARKET, BY INTERFACE, 2020-2027 (USD MILLION) 72

TABLE 45 ASIA-PACIFIC: FRAM MARKET, BY APPLICATION, 2020-2027 (USD MILLION) 73

TABLE 46 CHINA: FRAM MARKET, BY PRODUCT TYPE, 2020-2027 (USD MILLION) 73

TABLE 47 CHINA: FRAM MARKET, BY INTERFACE, 2020-2027 (USD MILLION) 74

TABLE 48 CHINA: FRAM MARKET, BY APPLICATION, 2020-2027 (USD MILLION) 74

TABLE 49 JAPAN: FRAM MARKET, BY PRODUCT TYPE, 2020-2027 (USD MILLION) 75

TABLE 50 JAPAN: FRAM MARKET, BY INTERFACE, 2020-2027 (USD MILLION) 75

TABLE 51 JAPAN: FRAM MARKET, BY APPLICATION, 2020-2027 (USD MILLION) 76

TABLE 52 INDIA: FRAM MARKET, BY PRODUCT TYPE, 2020-2027 (USD MILLION) 76

TABLE 53 INDIA: FRAM MARKET, BY INTERFACE, 2020-2027 (USD MILLION) 77

TABLE 54 INDIA: FRAM MARKET, BY APPLICATION, 2020-2027 (USD MILLION) 77

TABLE 55 SOUTH KOREA: FRAM MARKET, BY PRODUCT TYPE, 2020-2027 (USD MILLION) 78

TABLE 56 SOUTH KOREA: FRAM MARKET, BY INTERFACE, 2020-2027 (USD MILLION) 78

TABLE 57 SOUTH KOREA: FRAM MARKET, BY APPLICATION, 2020-2027 (USD MILLION) 79

TABLE 58 REST OF ASIA-PACIFIC: FRAM MARKET, BY PRODUCT TYPE, 2020-2027 (USD MILLION) 79

TABLE 59 REST OF ASIA-PACIFIC: FRAM MARKET, BY INTERFACE, 2020-2027 (USD MILLION) 80

TABLE 60 REST OF ASIA-PACIFIC: FRAM MARKET, BY APPLICATION, 2020-2027 (USD MILLION) 80

TABLE 61 MIDDLE EAST AND AFRICA: FRAM MARKET, BY COUNTRY, 2020-2027 (USD MILLION) 81

TABLE 62 MIDDLE EAST AND AFRICA: FRAM MARKET, BY PRODUCT TYPE, 2020-2027 (USD MILLION) 82

TABLE 63 MIDDLE EAST AND AFRICA: FRAM MARKET, BY INTERFACE, 2020-2027 (USD MILLION) 83

TABLE 64 MIDDLE EAST AND AFRICA: FRAM MARKET, BY APPLICATION, 2020-2027 (USD MILLION) 84

TABLE 65 UAE: FRAM MARKET, BY PRODUCT TYPE, 2020-2027 (USD MILLION) 85

TABLE 66 UAE: FRAM MARKET, BY INTERFACE, 2020-2027 (USD MILLION) 85

TABLE 67 UAE: FRAM MARKET, BY APPLICATION, 2020-2027 (USD MILLION) 86

TABLE 68 SOUTH AFRICA: FRAM MARKET, BY PRODUCT TYPE, 2020-2027 (USD MILLION) 86

TABLE 69 SOUTH AFRICA: FRAM MARKET, BY INTERFACE, 2020-2027 (USD MILLION) 87

TABLE 70 SOUTH AFRICA: FRAM MARKET, BY APPLICATION, 2020-2027 (USD MILLION) 87

TABLE 71 SAUDI ARABIA: FRAM MARKET, BY PRODUCT TYPE, 2020-2027 (USD MILLION) 88

TABLE 72 SAUDI ARABIA: FRAM MARKET, BY INTERFACE, 2020-2027 (USD MILLION) 88

TABLE 73 SAUDI ARABIA: FRAM MARKET, BY APPLICATION, 2020-2027 (USD MILLION) 89

TABLE 74 REST OF MIDDLE EAST AND AFRICA: FRAM MARKET, BY PRODUCT TYPE, 2020-2027 (USD MILLION) 89

TABLE 75 REST OF MIDDLE EAST AND AFRICA: FRAM MARKET, BY INTERFACE, 2020-2027 (USD MILLION) 90

TABLE 76 REST OF MIDDLE EAST AND AFRICA: FRAM MARKET, BY APPLICATION, 2020-2027 (USD MILLION) 90

TABLE 77 CENTRAL AND SOUTH AMERICA: FRAM MARKET, BY COUNTRY, 2020-2027 (USD MILLION) 91

TABLE 78 CENTRAL AND SOUTH AMERICA: FRAM MARKET, BY PRODUCT TYPE, 2020-2027 (USD MILLION) 92

TABLE 79 CENTRAL AND SOUTH AMERICA: FRAM MARKET, BY INTERFACE, 2020-2027 (USD MILLION) 93

TABLE 80 CENTRAL AND SOUTH AMERICA: FRAM MARKET, BY APPLICATION, 2020-2027 (USD MILLION) 94

TABLE 81 BRAZIL: FRAM MARKET, BY PRODUCT TYPE, 2020-2027 (USD MILLION) 95

TABLE 82 BRAZIL: FRAM MARKET, BY INTERFACE, 2020-2027 (USD MILLION) 95

TABLE 83 BRAZIL: FRAM MARKET, BY APPLICATION, 2020-2027 (USD MILLION) 96

TABLE 84 ARGENTINA: FRAM MARKET, BY PRODUCT TYPE, 2020-2027 (USD MILLION) 96

TABLE 85 ARGENTINA: FRAM MARKET, BY INTERFACE, 2020-2027 (USD MILLION) 97

TABLE 86 ARGENTINA: FRAM MARKET, BY APPLICATION, 2020-2027 (USD MILLION) 97

TABLE 87 COLOMBIA: FRAM MARKET, BY PRODUCT TYPE, 2020-2027 (USD MILLION) 98

TABLE 88 COLOMBIA: FRAM MARKET, BY INTERFACE, 2020-2027 (USD MILLION) 98

TABLE 89 COLOMBIA: FRAM MARKET, BY APPLICATION, 2020-2027 (USD MILLION) 99

TABLE 90 REST OF CENTRAL AND SOUTH AMERICA: FRAM MARKET, BY PRODUCT TYPE, 2020-2027 (USD MILLION) 99

TABLE 91 REST OF CENTRAL AND SOUTH AMERICA: FRAM MARKET, BY INTERFACE, 2020-2027 (USD MILLION) 100

TABLE 92 REST OF CENTRAL AND SOUTH AMERICA: FRAM MARKET, BY APPLICATION, 2020-2027 (USD MILLION) 100

TABLE 93 NEW PRODUCT LAUNCH/SERVICE DEPLOYMENT 103

TABLE 94 FUJITSU: PRODUCTS/SOLUTIONS/SERVICES OFFERED 108

TABLE 95 FUJITSU: KEY DEVELOPMENTS 109

TABLE 96 ROHM CO., LTD. PRODUCTS/SOLUTIONS/SERVICES OFFERED 113

TABLE 97 ROHM CO., LTD.: KEY DEVELOPMENTS 113

TABLE 98 TEXAS INSTRUMENTS: PRODUCTS/SOLUTIONS/SERVICES OFFERED 117

TABLE 99 TEXAS INSTRUMENTS: KEY DEVELOPMENTS 117

TABLE 100 CYPRESS SEMICONDUCTOR CORPORATION: PRODUCTS/SOLUTIONS/SERVICES OFFERED 122

TABLE 101 CYPRESS SEMICONDUCTOR CORP.: KEY DEVELOPMENTS 123

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[shunlongwei]

Microchip Technology Https://www.slw-ele.com; Email: sales@shunlongwei.com

Microchip Technology

https://en.wikipedia.org/wiki/Microchip_Technology Microchip Technology Inc. is an American publicly-listed corporation that is a manufacturer of Microcontroller, mixed-signal, analog and Flash-IP integrated circuits. Its products include microcontrollers (PIC, dsPIC, AVR and SAM), Serial EEPROM devices, Serial SRAM devices, embedded security devices, radio frequency (RF) devices, thermal, power and battery management analog devices, as well as Linear, interface and wireless solutions. Examples of these solutions include USB, zigbee, MiWi, LoRa, SIGFOX and Ethernet.

  Corporate headquarters are located in Chandler, Arizona, with wafer fabs in Tempe, Arizona, Gresham, Oregon, and Colorado Springs, Colorado, assembly/test facilities in Chachoengsao, Thailand and Calamba, Philippines. Sales for the fiscal year ending on March 31, 2018 were $3.981 billion.

History

Microchip Technology was founded in 1987 when General Instrument spun off its microelectronics division as a wholly owned subsidiary. Microchip Technology became an independent company in 1989 when it was acquired by a group of venture capitalists, and went public in 1993.

  In April 2009, Microchip Technology announced the nanoWatt XLP Microcontrollers, claiming the world's lowest sleep current. Microchip Technology had sold more than 6 billion microcontrollers as of 2009.

  In April 2010, Microchip acquired Silicon Storage Technology (SST), and sold several SST flash memory assets to Greenliant Systems in May that year.

  As of 2011, Microchip Technology ships over a billion processors every year. In September 2011, Microchip Technology shipped the 10 billionth PIC microcontroller.

  In August 2012, Microchip acquired Standard Microsystems Corporation (SMSC). Among SMSC's assets were those it had previously acquired from Symwave, a start-up that specialized in USB 3.0 chips, and two hi-fi wireless audio companies — Kleer Semiconductor and Wireless Audio IP BV.

  In January 2016, Microchip agreed to buy Atmel for $3.56 billion. JPMorgan Chase advised Microchip while Qatalyst Partners advised Atmel.

  In March 2018, Microchip acquired Microsemi Corporation (NASDAQ: MSCC). The acquisition price represents a total equity value of about $8.35 billion, and a total enterprise value of about $10.15 billion, after accounting for Microsemi’s cash and investments, net of debt, on its balance sheet at December 31, 2017.

  Products

Microchip develops a wide range of microcontrollers and integrated circuits (ics), for the hobbyist and professional markets.

  Microcontrollers

Microchip is widely known for their line of PIC microcontrollers, and their MCU-related product line includes:microcontroller

  PIC microcontrollers

8-bit mcus - PIC10, PIC12, PIC16, PIC18

16-bit MCUs - PIC24, dsPIC

32-bit MCUs - PIC32MX, PIC32MZ

Legacy intel MCS-51 MCUs

KEELOQ MCUs for security applications

rfPIC MCUs for wireless sensor applications

AVR microcontrollers

tinyAVR MCUs

megaAVR MCUs

AVR XMEGA MCUs

SAM Arm-based microcontrollers and microprocessors

Computer software

MPLAB IDE

MPLAB Xpress

C and C++ compilers for PIC/dsPIC MCUs

Code libraries for PIC/dsPIC MCUs

Atmel START for AVR and SAM MCUs

Development hardware

MPLAB series (debuggers & programmers for professionals)

PICkit series (programmers for hobbyists and students)

Integrated circuits

The Microchip product line of integrated circuits include:

  Memory storage devices

Serial EEPROM chips

Serial SRAM chips

Serial Flash chips

Parallel Flash chips

Serial NVRAM chips

Interface devices

USB controllers

ZigBee/MiWi controllers

CAN/LIN controllers

Ethernet controllers

Power management devices

Battery charge controllers (Li-Ion, NiMH, Multi-Chemistry)

Power MOSFETs

Voltage regulators

Motor drivers

PWM-based controllers

DC motor controllers

BLDC motor controllers

Touch sensing

mTouch (capacitive sensor technology)

RightTouch (turn-key capacitive sensor technology)

GestIC (3D Tracking and gesture detection technology)

Haptics (Eccentric Rotating Mass (ERM) actuators)

Ultrasound devices

Ultrasound switches

Ultrasound transmitters

Pre：NXP Semiconductors

Next：Kyocera Corporation

[amaxchipamaxchip]

stm32f103c8t6 Microcontroller

A microcontroller is an integrated circuit device. It uses a microprocessor unit (MPU), memory, and peripheral to control other portions of an electronic system. It is a small, low-cost, and self-contained computer-on-a-chip used as an embedded system. A few microcontrollers may utilize four-bit expressions and work at clock rate frequencies,z little measure of RAM, programmable ROM, and flash memory, parallel and serial I/O, timers and signal generators, digital to analog, and analog to digital conversion. Microcontrollers usually must have low-power requirements since many devices they control are battery-operated. The dominant part of microcontrollers being used nowadays is implanted in other apparatus.

In stm32f103c8t6,theSTM32means ARM-based 32-bit microcontroller, F indicates general purpose usage, 103 is the performance line,  C shows the number of pins (C=48 pins), 8 is the Kbytes of Flash memory ( 8 = 64), T indicates the package type ( T = LQFP), and 6 is the operating temperature ( 6 = Industrial temperature range, –40 to 85 °C)

 The stm32f103c8t6incorporates an ARM® 32-bit Cortex®-M3 CPU Core capable of operating at 72 MHz maximum frequency and can perform Single-cycle multiplication hardware division. The stm32f103c8t6has 64 or 128 Kbytes of Flash memory and 20 Kbytes of Static random-access memory (SRAM). The stm32f103c8t6has a built-in clock, reset and supply management system. It has a 2.0 to 3.6 V application supply and I/O, a 4-to-16 MHz crystal oscillator, an internal 8 MHz factory-trimmed RC and a 32 kHz oscillator for RTC with calibration. The stm32f103c8t6switches between Sleep, Stop, and Standby, and it has a VBAT supply for RTC and backup registers. The stm32f103c8t6offers 2 x 12-bit, one µs A/D converters (up to 16 channels) with a Conversion range of 0 to 3.6 V and a Temperature sensor. The stm32f103c8t6has aseven-channel DMA controller and supports Peripherals such as timers, ADC, SPIs, I 2Cs, and USARTs. Up to 80 fast I/O ports are all mappable on 16 external interrupt vectors and almost all 5 V-tolerant. The stm32f103c8t6has a serial wire debug mode(SWD) and JTAG interfaces. It also has three 16-bit timers, two watchdog timers, a SysTick timer 24-bit down counter, and a 16-bit motor control PWM timer with deadtime generation and emergency stop. The stm32f103c8t6supports up to 9 communication interfaces which include 2 x I2C interfaces, 3 USARTs, 2 SPIs (18 Mbit/s), CAN interface (2.0B Active) and a USB 2.0 full-speed interface.The stm32f103c8t6operates from a 2.0 to 3.6 V power supply. They are available in both the –40 to +85 °C temperature range and the –40 to +105 °C extended temperature range. The stm32f103c8t6is used in the design of low-power applications due to its comprehensive set of power-saving modes.

These features make the stm32f103c8t6medium-density performance microcontroller suitable for a wide range of applications such as motor drives, application control, medical and handheld equipment, PC and gaming peripherals, GPS platforms, industrial applications, PLCs, inverters, printers, scanners, alarm systems, video intercoms, and HVACs.

[allthingsverification]

RAM Vrs CAM

Difference between Random Access Memory (RAM) and Content Addressable Memory (CAM)

Last Updated : 13 May, 2019

RAM: Random Access Memory (RAM) is used to read and write. It is the part of primary memory and used in order to store running applications (programs) and program’s data for performing operation. It is mainly of two types: Dynamic RAM (or DRAM) and Static RAM (or SRAM).

CAM: Content Addressable Memory (CAM) is also known as Associative Memory, in which the user supplies data word and associative memory searches its entire memory and if the data word is found, It returns the list of addresses where that data word was located.

The difference table is given below on the basis of their properties:

S.NORAM MemoryAssociative Memory(CAM)

{1st line for RAM, 2nd for CAM}

1.RAM stands for Random Access Memory.

1. It stands for Content Addressable Memory.

2.In RAM, the user supplies a memory address and RAM returns data word stored at the address.

2. In associative memory, the user supplies data word and associative memory searches its entire memory.

3.The price of RAM is low as compared to Associative memory.

3. It is expensive than RAM.

4.It is used to store running applications(programs) and program’s data for performing operation.

4. It is widely used in database management system.

5.This is suitable for algorithm based search via PRAM. 

5. PRAM stands for Parallel-RAM.This is suitable for parallel search.

6.If the data word is found, RAM returns the data word.

6. If the data word is found, It returns the list of addresses where that data word was located.