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The 6th IBM IEEE CAS/EDS AI Compute Symposium was held hybrid at the T. J. Watson Research Center on 28 November 2023. The event was extremely successful and well attended by over 2000 folks from all over the world (in-person and virtual). The symposium featured 8 distinguished speakers (7 from industry and 1 from academia), over 30 student in-person posters, best poster awards, and a panel discussion. The registration list spanned citizens of 53 countries. The theme of the symposium, “From Chips to Chiplets,” turned out to be an opportune and important topic for the current semiconductor industry direction. The symposium served as an educational as well as a brainstorming session for industry/academia/students across the world. The symposium covered a range of topics from emerging device technology, innovative circuits, chip and chiplet architecture, advanced packaging technologies, such as 2D to 3D packaging elements, and how these topics drive the rapid growth of AI and generative AI. Dr. Rajiv Joshi, General Chair and IEEE Life Fellow opened the symposium with welcoming remarks along with the goals and accomplishments of this symposium under the auspices of CAS and IBM. [ABSTRACT FROM AUTHOR]

The biological challenges facing humanity are complex, multi-factorial, and are intimately tied to the future of our health, welfare, and stewardship of the Earth. Tackling problems in diverse areas, such as agriculture, ecology, and health care require linking vast datasets that encompass numerous components and spatio-temporal scales. Here, we provide a new framework and a road map for using experiments and computation to understand dynamic biological systems that span multiple scales. We discuss theories that can help understand complex biological systems and highlight the limitations of existing methodologies and recommend data generation practices. The advent of new technologies such as big data analytics and artificial intelligence can help bridge different scales and data types. We recommend ways to make such models transparent, compatible with existing theories of biological function, and to make biological data sets readable by advanced machine learning algorithms. Overall, the barriers for tackling pressing biological challenges are not only technological, but also sociological. Hence, we also provide recommendations for promoting interdisciplinary interactions between scientists. [ABSTRACT FROM AUTHOR]

In this paper, it is shown empirically and through simulation that transistor mismatch due to random dopant fluctuation is a function of the well and halo design of the transistor, and that, contrary to conventional expectation, low-threshold transistors can have larger mismatch than higher threshold transistors. The complex dependence of mismatch on well and halo profiles suggests the need for the extension of the conventional Pelgrom approach to characterizing mismatch for a given technology and also suggests means of optimizing mismatch for analog applications. A set of screening criteria for mismatch data analysis are presented to verify that conclusions drawn from the standard deviation of a distribution may be properly applied. [ABSTRACT FROM PUBLISHER]

The crystalline quality of silicon-on-insulator fabricated by a wafer bonding technique was examined by spectroscopic ellipsometry and Raman spectroscopy. The detailed modeling of the experimental ellipsometric data yields information about structural defects in the silicon-on-insulator layer. The dielectric function of the silicon on insulator that best models the experimental ellipsometric data includes a physical mixture of crystalline silicon and about 4%–7% of amorphous silicon, suggesting a slight lack of long-range order of the silicon atoms in the silicon-on-insulator layer. The use of a dielectric function other than that of pure crystalline silicon is supported by Raman spectroscopic results that indicate the presence of structural defects in the silicon-on-insulator layer. These structural defects may be due to the effects of hydrogen implantation used in the fabrication process of silicon-on-insulator, and/or the strain imposed by the lattice mismatch between the buried layer and the silicon substrate, and subsequently relaxed under high-temperature annealing. [ABSTRACT FROM AUTHOR]

We have made ferroelectric memory capacitors by depositing a SrBi[sub 2]Ta[sub 2]O[sub 9] thin film on a Si substrate separated by an ultrathin buffer layer of silicon nitride film. The hysteresis in the capacitance–voltage curves suggests a sizable memory window of 2 V with a programming voltage swing of ±7 V. The switching time is estimated to be on the order of nanosecond based on the results of a one-shot pulse experiment. The results from the fatigue test indicate a slight degradation of the memory window after 10[sup 11] switching cycles. These properties are encouraging for the development of ferroelectric memory transistors. © 1998 American Institute of Physics. [ABSTRACT FROM AUTHOR]

The goal of this vision paper is to investigate the possible role that advanced machine learning techniques, especially deep learning (DL), could play in the reintegration of various biological disciplines. To achieve this goal, a series of operational, but admittedly very simplistic, conceptualizations have been introduced: Life has been taken as a multidimensional phenomenon that inhabits three physical dimensions (time, space, and scale) and biological research as establishing connection between different points in the domain of life. Each of these points hence denotes a position in time, space, and scale at which a life phenomenon of interest takes place. Using these conceptualizations, fragmentation of biology can be seen as the result of too few and especially too short-ranged connections. Reintegrating biology could then be accomplished by establishing more, longer ranged connections. DL methods appear to be very well suited for addressing this particular need at this particular time. Notwithstanding the numerous unsubstantiated claims regarding the capabilities of AI, DL networks represent a major advance in the ability to find complex relationships inside large data sets that would have not been accessible with traditional data analytic methods or to a human observer. In addition, ongoing advances in the automation of taking measurements from phenomena on all levels of biological organization continue to increase the number of large quantitative data sets that are available. These increasingly common data sets could serve as anchor points for making long-range connections by virtue of DL. However, connections within the domain of life are likely to be structured in a highly nonuniform fashion and hence it is necessary to develop methods, for example, theoretical, computational, and experimental, to determine linkage of biological data sets most likely to provide useful insights on a biological problem using DL. Finally, specific DL approaches and architectures should be developed to match the needs of reintegrating biology., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology.)

The biological challenges facing humanity are complex, multi-factorial, and are intimately tied to the future of our health, welfare, and stewardship of the Earth. Tackling problems in diverse areas, such as agriculture, ecology, and health care require linking vast datasets that encompass numerous components and spatio-temporal scales. Here, we provide a new framework and a road map for using experiments and computation to understand dynamic biological systems that span multiple scales. We discuss theories that can help understand complex biological systems and highlight the limitations of existing methodologies and recommend data generation practices. The advent of new technologies such as big data analytics and artificial intelligence can help bridge different scales and data types. We recommend ways to make such models transparent, compatible with existing theories of biological function, and to make biological data sets readable by advanced machine learning algorithms. Overall, the barriers for tackling pressing biological challenges are not only technological, but also sociological. Hence, we also provide recommendations for promoting interdisciplinary interactions between scientists., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology.)