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Semico is a semiconductor marketing & consulting research company located in Phoenix, Arizona. We offer custom consulting, portfolio packages, individual market research studies and premier industry conferences.

Average Design Cost for Basic SoCs Across all Geometries was $1.7M in 2017, says Semico Research

The semiconductor industry today is faced with several substantial issues-not the least of which are the continuing rise in design costs for complex SoCs, the decrease in the incidence of first-time-right designs and the increase in the design cycle time against shrinking market windows and decreasing product life cycles. An additional factor has now been added to SoC design costs with the emergence of very complicated software applications intended to run on the SoC silicon.

Semico Fab Database 2018 Summary Now Available

Semico's Fab Database report is a great resource for tracking changes among advanced as well as mature fabs.  You can also sort the data by region or by type of products.  Semico's report includes updates on fabs owned by foundries, memory manufacturers, IDMs, and research facilities.  The database itself includes detailed information about each fab, including operating status, location, process, products, wafer size, capacity, and more.  Semico updates it biannually with a summary of

SoC Silicon and Software 2018 Design Cost Analysis: How Rising Costs Impact SoC Design Starts

The complex SoC marketplace is undergoing a rapid evolution in response to market pressures to provide better solutions.  This market, however, is under assault on several fronts due to many issues with the effect of rising design costs for Advanced Performance Multicore SoCS and Value Multicore SoCs.  This report covers current complex SoC market trends and drivers, focusing on design costs during the first year that a new node is available.  Embedded software design costs are covered, as well as the impacts of rising design costs on SoC design starts, by process node, from 180nm down to 3

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2018 Total Wafer Demand Expected To Reach 115.1 Million 300mm Wafer Equivalents, Says Semico Research

Semiconductor revenues are expected to increase 12.8% in 2018 as a result of continued strong memory prices. Units are expected to grow 7.2%. The forecast is based on moderate smartphone sales with a possible return to lower memory prices in the second half of the year.

Semico Wafer Demand: Q2 2018 Highlights

The Wafer Demand Summary and Assumptions is a quarterly publication. It includes an excel spreadsheet with annual wafer demand by product by technology from 2010-2021. Product categories include DRAM, SRAM, NAND, NOR, Other Non-volatile, MPU, MCU, DSP, Computing Micro Logic, Communications, Other Micro Logic, Programmable Logic, Standard Cell, Gate Array, Analog, Discrete, Optoelectronics, Sensors and Digital Bipolar. In addition, there is a summary write-up providing the major assumptions behind the forecast and changes from the previous quarter.

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Semico Fab Database: Update Summary First Half 2018

Semico tracks almost 1,000 semiconductor fabs in its Fab Database.  The database includes detailed information about the fabs, including the operating status of the fab, its location, process and products, wafer size and capacity, and more.  The other document included with the database is a Word file containing a summary of updates made to fabs by company type:  Memory, Foundries, and Other.  

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Artificial Intelligence: Powering the Next Generation of Processors

Artificial Intelligence (AI) functionality has emerged into the semiconductor market with many companies participating at all levels of the semiconductor, software, services and IP industries offering a variety of products. The emergence of the AI market represents the 'next big thing' for both the semiconductor industry as well as the overall economy. Deployment has already started in factory floor environments delivering better control and maintenance over a multitude of industrial processes and products.

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Energy Harvesting Semiconductor Content to Approach $3.4 Billion by 2022, says Semico Research

The term energy harvesting, also known as power scavenging, is used to describe the creation of energy derived from a variety of external sources such as solar power, thermal energy, wind energy, kinetic energy or electromagnetic sources. Energy harvesters accumulate the wasted energy in a system, such as heat given off by motors or semiconductors, or the vibrations of motors or other moving objects.

Energy Harvesting: Reaping the Abundant Market

In its recent report “Energy Harvesting: Reaping the Abundant Market” (MP112-18), Semico Research examines the market opportunity for energy harvesting outside of large solar installations and commercial power generation.  A broad range of markets will employ energy harvesting to either replace batteries or extend battery life. These applications cover wireless sensor nodes (WSN) for bridges, infrastructure, building automation and controls, home automation (including lighting, security and environmental), automotive applications, cell phones, wearables and other consumer electronics.

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AI Accelerating Discovery

In early April 2018, the Materials Research Society held their spring meeting and exhibit at the Phoenix, Arizona convention center.  With over 110 symposium presentations, it was difficult to select which sessions to attend.  But one forum caught my eye, “AI for Materials Development”.  These days AI seems to be everywhere.  
 
As we all speculate about the impact of AI on autonomous driving and the next killer app, Carla Gomes, Professor of Computer Science and director of the Institute for Computational Sustainability at Cornell University, is focusing on large-scale constraint-based reasoning.  She pointed out that AI still can’t compete with good ol’ human common sense.  Human reasoning and inference planning are still lacking in most AI systems.  One of the key fundamentals of AI is building a neural network that resembles the human brain.  Even with the advancements of 7nm silicon technology, this is a daunting task, not to mention the complexities of software algorithms to mimic the human thought and decision process.
 
But in the world of materials development, AI excels.  By integrating material experimentation and AI, the discovery of new materials and the application of materials in the real world is progressing at an accelerated pace.  AI is capable of developing the hypotheses and—along with robotics—is following through with new scientific discovery. 
 

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