In Honor of Nobel Laureate Prof. Ferid Murad

Abstract Submission Open! About 500 abstracts submitted from about 60 countries

Featuring 9 Nobel Laureates and other Distinguished Guests

Abstract Submission

Gurtej Sandhu

Micron Technology

The Future Of Memory Chip Technology
Virk International Symposium (Intl Symp on Physics, Technology & Interdisciplinary Research for Sustainable Development)

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Without advances in how the world physically stores and retrieves data, today’s most useful devices and algorithms would not exist. The dominant memory chip technologies such as NAND Flash and DRAM rode the wave of innovations in materials, process and device technologies to scale down the path of Moore’s law. Although physical scaling is becoming increasingly difficult, the forces and market pull driving cost, power and density scaling are growing relentlessly. The amount of memory in systems for example is increasing geometrically and the applications continue to diversify and expand from traditional handheld devices and large data centers.
A commitment to innovation and creativity is needed to help fuel the next generation of technologies such as self-driving cars, space exploration and artificial intelligence (AI), which sounded like science fiction not so long ago. In addition, several flavors of new memory technologies based on alternate state variables are under development. This is driving unprecedented demand for new materials research and innovation. The unique set of requirements span from new device physics functionality at nanometer dimension scale to ability to cover complex 3D structure with large surface areas. This opens a new era of materials and process development space not seen before in the microelectronics chip industry. A robust strategy is needed to provide a framework in which scientists and engineers can work to reduce the likelihood of the effects of trace elements, molecules or ligands on device and structural performance and assure environmental sustainability. Examples will be used to illustrate the overhead in modeling, analytics, logistics and data management required to be successful.