Atmel Corporation was a designer and manufacturer of semiconductors before being acquired by Microchip Technology in 2016. It was founded in 1984; the company focuses on embedded systems built around microcontrollers. Its products include microcontrollers radio frequency devices including Wi-Fi, EEPROM, flash memory devices and asymmetric security chips, touch sensors and controllers, application-specific products. Atmel supplies its devices as standard products, application-specific integrated circuits, or application-specific standard product depending on the requirements of its customers. Atmel serves applications including consumer, computer networking, medical, automotive and military, it specializes in microcontroller and touch systems for embedded systems. Atmel's corporate headquarters is in San Jose, California, in the North San Jose Innovation District. Other locations include Norway. Atmel makes much of its product line at vendor fabrication facilities, it owns a facility in Colorado Springs, Colorado that manufactures its XSense line of flexible touch sensors.
In 2016, Microchip agreed to buy Atmel for US$3.6 billion in a deal brokered by JPMorgan Chase and Qatalyst. Atmel Corporation was founded by George Perlegos. Atmel was an acronym for “advanced technology for memory and logic”. Perlegos had worked in the memory group of Intel in the 1970s and had co-founded Seeq Technology to manufacture EPROM memory. Using only US$30,000 in capital, Atmel was operated as a fabless company, using Sanyo and General Instrument to make the chip wafers; the first Atmel memory products used less power than competitors. Customers included Motorola and Ericsson. In 1987, Intel sued Atmel for patent infringement. Rather than fight the patent claim, Atmel redesigned its products to use different intellectual property; these had better performance and lower power consumption. In addition, Atmel entered the flash memory business that Intel had focused on. Atmel used US$60 million in venture capital for the 1989 purchase of a fabrication facility from Honeywell in Colorado Springs.
Atmel invested another US$30 million in manufacturing technology. In 1991, Atmel expanded the Colorado facility after acquiring Concurrent Logic, a field-programmable gate array manufacturer; the company made its initial public offering in 1991. 1994 saw. The first Atmel flash memory microcontroller was based on the Intel 8051; the controller executed an instruction for every clock cycle, as opposed to the 12 cycles that legacy 8051 parts required. In 1994, Atmel purchased the EEROM assets of Seeq Technology. In 1995, Atmel was among the first companies to license the ARM architecture, creating its AT91 family of devices, followed by the SAM family, more a full selection of Cortex-based solutions, including ones based on the ultra-low-power ARM Cortex-M4. Atmel now has dozens of families of ARM-based devices. In 1995, Atmel acquired the pan-European chipmaker European Silicon Structures and thus gained a fabrication facility in Rousset, France. Atmel built a new fab alongside the existing ES2 fab.
This business unit was named Atmel-ES2. Atmel acquired Digital Research in Electronic Acoustics and Music in 1996. Atmel formed a design team in Trondheim, Norway to develop the Atmel AVR line of RISC microcontrollers; this team combined technology of former students at the Norwegian University of Science and Technology with Atmel's expertise in flash memory. These 8-bit Harvard architecture chips were first developed in 1996; the AVR chip is the basis of most Arduino open-source development boards. In 1998, Atmel purchased part of TEMIC from Vishay Intertechnology, which provided them with a fab in Germany as well as part of MHS from Vishay that gave them a fab in Nantes, France. In September 2000, Atmel acquired a fabrication plant in North Tyneside, from Siemens, via a £28 million grant from the UK government and paying Siemens around US$35 million. Atmel streamlined operations with a strategy called "fab-lite"; this started in 2005. In February 2006, Steven Laub in August president and chief executive officer.
Under Laub Atmel divested business lines. Atmel announced the sale of its North Tyneside facility on October 8, 2007; the manufacturing equipment was sold to Taiwan Semiconductor Manufacturing Company, Ltd. and the property and associated land to Highbridge Business Park Limited. In 2008, Atmel sold their fab in Germany to Tejas semiconductor. In 2010, Atmel received approval from the French government to sell its fab to Germany-based LFoundry GmbH, while retaining their design center there. Atmel completed the sale of their Secure Microcontroller Solutions smart card business to INSIDE Secure. In February 2011, Atmel sold its Digital Research in Electronics and Music business, which sold products for karaoke and other entertainment machines, for US$2.3 million. Atmel's DataFlash serial interface flash memory products were sold to Adesto Technologies in October 2012; as Atmel divested several fabs and ancillary business lines, Laub oversaw acquisitions. One strategy was to participate in the touch screen market, both in the semi
In the field of computational chemistry, energy minimization is the process of finding an arrangement in space of a collection of atoms where, according to some computational model of chemical bonding, the net inter-atomic force on each atom is acceptably close to zero and the position on the potential energy surface is a stationary point. The collection of atoms might be a single molecule, an ion, a condensed phase, a transition state or a collection of any of these; the computational model of chemical bonding might, for example, be quantum mechanics. As an example, when optimizing the geometry of a water molecule, one aims to obtain the hydrogen-oxygen bond lengths and the hydrogen-oxygen-hydrogen bond angle which minimize the forces that would otherwise be pulling atoms together or pushing them apart; the motivation for performing a geometry optimization is the physical significance of the obtained structure: optimized structures correspond to a substance as it is found in nature and the geometry of such a structure can be used in a variety of experimental and theoretical investigations in the fields of chemical structure, chemical kinetics and others.
But not always, the process seeks to find the geometry of a particular arrangement of the atoms that represents a local or global energy minimum. Instead of searching for global energy minimum, it might be desirable to optimize to a transition state, that is, a saddle point on the potential energy surface. Additionally, certain coordinates might be fixed during the optimization; the geometry of a set of atoms can be described by a vector of the atoms' positions. This could be the set of the Cartesian coordinates of the atoms or, when considering molecules, might be so called internal coordinates formed from a set of bond lengths, bond angles and dihedral angles. Given a set of atoms and a vector, r, describing the atoms' positions, one can introduce the concept of the energy as a function of the positions, E. Geometry optimization is a mathematical optimization problem, in which it is desired to find the value of r for which E is at a local minimum, that is, the derivative of the energy with respect to the position of the atoms, ∂E/∂r, is the zero vector and the second derivative matrix of the system, i j known as the Hessian matrix, which describes the curvature of the PES at r, has all positive eigenvalues.
A special case of a geometry optimization is a search for the geometry of a transition state. The computational model that provides an approximate E could be based on quantum mechanics, force fields, or a combination of those in case of QM/MM. Using this computational model and an initial guess of the correct geometry, an iterative optimization procedure is followed, for example: calculate the force on each atom if the force is less than some threshold, finish otherwise, move the atoms by some computed step ∆r, predicted to reduce the force repeat from the start As described above, some method such as quantum mechanics can be used to calculate the energy, E, the gradient of the PES, that is, the derivative of the energy with respect to the position of the atoms, ∂E/∂r and the second derivative matrix of the system, ∂∂E/∂ri∂rj known as the Hessian matrix, which describes the curvature of the PES at r. An optimization algorithm can use some or all of E, ∂E/∂r and ∂∂E/∂ri∂rj to try to minimize the forces and this could in theory be any method such as gradient descent, conjugate gradient or Newton's method, but in practice, algorithms which use knowledge of the PES curvature, the Hessian matrix, are found to be superior.
For most systems of practical interest, however, it may be prohibitively expensive to compute the second derivative matrix, it is estimated from successive values of the gradient, as is typical in a Quasi-Newton optimization. The choice of the coordinate system can be crucial for performing a successful optimization. Cartesian coordinates, for example, are redundant since a non-linear molecule with N atoms has 3N–6 vibrational degrees of freedom whereas the set of Cartesian coordinates has 3N dimensions. Additionally, Cartesian coordinates are correlated, that is, the Hessian matrix has many non-diagonal terms that are not close to zero; this can lead to numerical problems in the optimization, for example, it is difficult to obtain a good approximation to the Hessian matrix and calculating it is too computationally expensive. However, in case that energy is expressed with standard force fields, computationally efficient methods have been developed able to derive analytically the Hessian matrix in Cartesian coordinates while preserving a computational complexity of the same order to that of gradient computations.
Internal coordinates tend to be less correlated but are more difficult to set-up and it can be difficult to describe some systems, such as ones with symmetry or large condensed phases. Many modern computational chemistry software packages contain automatic procedures for the automatic generation of reasonable coordinate systems for optimization; some de
Claire L. Evans is an American singer and artist based in Los Angeles, California, she is the lead singer of the pop duo YACHT. Evans joined YACHT in 2008 after sharing a "mystical experience" with collaborator Jona Bechtolt and has recorded three albums, namely See Mystery Lights, Shangri-La and I Thought the Future Would Be Cooler with Bechtolt, she appeared as a guest on YACHT's third album I Believe in You. Your Magic Is Real. Known for her androgynous onstage persona as a performer, she has been called a "neo-Annie Lennox" by The New York Times. NPR music journalist Bob Boilen has referred to her as "one of the most striking performers I've seen in a rock band". In addition, Evans is a journalist and author of Broad Band: The Untold Story of the Women Who Made the Internet. With a popular science and culture blog titled Universe, hosted by National Geographic's Scienceblogs network, her essay for Universe "Moon Art: Fallen Astronaut" was anthologized in The Best Science Writing Online 2012.
She writes for Vice, The Guardian and Aeon. In August 2013, she became the editor-in-chief of OMNI Reboot, a new online version of the science magazine OMNI, she is the Futures Editor of Motherboard, Vice's technology and science website. She is a member of the feminist collective Deep Lab, she is the creator of the App 5 Every Day