• STMicroelectronics achieved a massive 22,2% year-over-year improvement to its net revenue, which totalled $2,23 billion for the first quarter of its 2018 financial year. The company reported that this growth was reflected by double-digit sales growth across all product groups and regions. Net income for the quarter was $239 million, or $0,26 diluted earnings per share (EPS).
• NXP Semiconductors delivered first quarter revenue of $2,27 billion, an increase of approximately 3% year-on-year, and a decrease of 8% as compared to the prior quarter, with the annual period comparison impacted by the divestment of the standard products business during the first quarter of 2017. The company is still awaiting regulatory approvals for its acquisition by Qualcomm; the purchase offer of $127,50 per share expires on 25 July 2018.
• Total revenue for ON Semiconductor’s first quarter of 2018 was $1377,6 million, down approximately 4% compared to the first quarter of last year and flat sequentially. At $139,6 million ($0,31 per diluted share), net income improved year-on-year by 79%.
• Silicon Labs’ first quarter revenue exceeded the high end of guidance at $205 million, up from $201 million in the prior quarter, and establishing a new all-time record for the company. The company expects revenue in the second quarter to be in the range of $211 million to $217 million.
• Quarter-on-quarter, revenue grew by 3% to 1,836 billion Euros for Infineon Technologies’ second quarter of 2018. The automotive and industrial power control segments reported significant growth, while the power management and multimarket, and chip card and security segments remained almost unchanged compared to the preceding quarter. Net income improved sequentially from 205 million Euros to 457 million Euros.
• Texas Instruments reported first-quarter revenue of $3,79 billion, net income of $1,37 billion and earnings per share of $1,35 for its first 2018 quarter. Revenue increased 11% from the same quarter a year ago, driven by strong demand for the company’s analog and embedded processing products in the industrial and automotive markets. The second-quarter outlook is for revenue in the range of $3,78 billion to $4,10 billion, and earnings per share between $1,19 and $1,39.
• Cypress Semiconductor announced first quarter revenue of $582,2 million, a 9,5% year-over-year increase, boosted by a rise in revenue from the automotive end market of 14,8%. Net income for the first quarter of 2018 was $9,08 million ($0,02 EPS) compared with a net loss of $43 million in the first quarter of 2017.
• Intel is selling its embedded software business, Wind River, to the global equity group TPG. The sale comes nine years after Intel acquired Wind River for $884 million in cash. The financial terms of the sale to TPG have not been disclosed.
• The Semiconductor Industry Association announced that worldwide sales of semiconductors reached $111,1 billion during the first quarter of 2018, an increase of 20% compared to the first quarter of 2017, but 2,5% less than the fourth quarter of 2017. Sales for the month of March 2018 came in at $37,0 billion, an increase of 20% compared to the March 2017 total of $30,8 billion and 0,7% more than the February 2018 total of $36,8 billion.
• According to the SEMI trade association, the global market for materials used to manufacture semiconductors grew 9,6% in 2017, and is projected to grow by another 4% in 2018 to reach an all-time high of $48,7 billion. For the eighth consecutive year, Taiwan, at $10,3 billion, was the largest consumer of semiconductor materials due to its large foundry and advanced packaging base. China solidified its hold on second spot, followed by South Korea and Japan.
• A benchmarking system for machine-learning applications, called MLPerf, is being developed as a collaboration between industry and academia. Expected to be ready for use in August this year, MLPerf has enjoyed research contributions from such institutions as Harvard and Stanford Universities, and is supported by leading tech companies including AMD, Baidu, Google and Intel. The benchmark’s approach will be to select a set of machine-learning problems, each defined by a dataset and quality target, then measure the wall clock time to train a model for each problem.
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