Penn Engineering Events

CBE Seminar: "Antibiotic Discovery by Means of Computers"
2020-10-28 15:00 - 16:00 (America/New_York) | Zoom – Email CBE for link
Abstract
Until now, the natural world has supplied us with antibiotics. Bacteria, however, are increasingly resistant to these drugs. The next generation of antibiotics will likely come not from nature but from computer-based discovery. Computer-driven approaches have the potential to outperform humans, as demonstrated for pattern recognition of images and text. In order for machines to discover novel drugs and optimize antimicrobial properties, they have to be able to understand, read and write molecules. In this talk, I will describe our efforts in developing computational approaches for antibiotic discovery. I will discuss how we trained a computer to execute a fitness function following Darwin’s algorithm of evolution to select for structures that interact with bacterial membranes, yielding the first artificial antimicrobials that kill bacteria both in vitro and in animal models. My lab has also developed pattern recognition algorithms to mine the human proteome, identifying throughout the body thousands of antibiotics encoded in proteins with unrelated biological function. Computer-made drugs may help to replenish our arsenal of effective drugs.

CIS Seminar: "Advances in Machine Learning Systems Research"
2020-10-27 15:00 - 16:00 | Zoom – Email CIS for link, cherylh@cis.upenn.edu
A long-standing grand challenge in computing is to enable machines to act autonomously and intelligently: to rapidly and repeatedly take appropriate actions based on information in the world around them. Driven by trends in the data economy, rapid progress in AI, and an increasingly programmable physical world we are at an inflection point that demands a new class of AI system. This new class of systems goes beyond training models at scale, to connecting models with the world, rendering predictions in real-time under heavy query load, adapting to new observations and contexts. These systems will need to be composable and elastically scalable to accommodate new technologies and variations in workloads. Operating in the physical world, observing intimate details of our lives, and making critical decisions, these systems must also be secure.
In this talk, I will present work in my group exploring advances in systems for prediction serving, autonomous driving, and how model design and system design interact. In particular, I will discuss some of the key trade-offs between time, accuracy, convergence, throughput, and security that govern how we design systems, train models, and make predictions that meet the demands of real-world applications.

ESE Seminar: "High-Frequency Power Conversion with Wide-Bandgap Semiconductors"
2020-10-27 13:00 - 14:00 | Zoom – Email ESE for Link jbatter@seas.upenn.edu
With the commercialization of wide-bandgap power semiconductors, multi-MHz switching frequencies are more compelling and critical to meet new applications demanding leaps in power density and efficiency. In the past, studies of these converters reported significant gaps between measured and modeled performance, often attributed to dynamic RDS,ON in GaN HEMTs. In particular, the power semiconductors – which often drive thermal constraints – dissipated much more power than expected, rendering designs based on simulated values unusable. In soft-switched converters, which dominate at MHz frequencies, the semiconductor’s output capacitor is resonantly charged and discharged once per switching cycle. Recently, multiple papers have found significant losses from this process in silicon and wide-bandgap devices, explaining the unexpected power dissipation. With these losses known, the MHz-frequency design space can be reopened – if designers are careful about semiconductor selection. In this talk, I will discuss how to select the right device across material (GaN, SiC, or Si), device technology (superjunction or trench), size (lower RDS,ON is not always better), and, in some cases, manufacturer. Further, I will show how this selection drives thermal design, input voltage selection, and novel circuit topologies in a variety of high-performance demonstrations from 6.78 MHz all the way to 40.68 MHz.

MEAM Seminar: "Data-driven Physics Discovery and Scale Bridging in Materials"
2020-10-27 10:30 - 12:00 | Zoom – Email MEAM for Link, peterlit@seas.upenn.edu
In this talk I will provide an overview of our recent work in data-driven methods—mainly machine learning—to enhance computational materials physics models. This body of work has proceeded along two main fronts. The first is system inference, where we seek to identify physical mechanisms via their mathematical signatures as differential or algebraic operators. Our approach of Variational System Identification leverages the weak form of partial differential equations to identify the physics underlying pattern formation, and the deformation mechanisms of soft materials. The framework of Variational System Identification has to address several challenges specific to experimental characterization of materials, such as data that is noisy, sparse, originates from different specimens, and spans dynamics to steady state regimes. The second front is in scale bridging, which we approach in the context of determining free energy functions. We have developed Integrable Deep Neural Networks, and active learning algorithms to combine data generated by Density Functional Theory calculations with cluster expansions and Monte Carlo computations to obtain free energy density functions. These are used in mechano-chemically coupled continuum methods to predict the evolution of microstructure in alloys.

PSOC Webinar: Joel Bader
2020-10-26 12:00 - 13:00 | PA
Talk title TBC
Physical Sciences in Oncology Center PSOC@Penn
Fall 2020 Webinar Series Mondays @ Noon (EST)
For webinar links, please contact manu@seas.upenn.edu

PICS Seminar: "Simulating solids like fluids: A fully Eulerian approach to fluid-structure interaction"
2020-10-23 14:00 - 15:00 | PA
Abstract: Fluids and solids tend to be addressed using distinct computational perspectives. Solid deformation is most commonly simulated with Lagrangian finite-element methods, whereas fluid flow is amenable to Eulerian-frame approaches such as finite difference and finite volume methods. Problems that mix fluid and solid behaviors simultaneously present interesting numerical challenges. Here we focus on fluid-structure interaction (FSI) problems, and discuss an emerging method called the Reference Map Technique, which allows us to simulate deformable solids on a fixed Eulerian grid. The key is to store and update the reference map field on the grid, which tracks the inverse motion. Using this technique to represent the solid phase, we can solve all phases of an FSI problem on a single fixed grid using fast update procedures very similar to those used in two-phase Navier-Stokes fluid simulations. Various solid constitutive behaviors can be used, such as nonlinear elasticity and plasticity. Systems of many submerged and interacting solids can be simulated, and, by activating the solids internally, we can simulate systems of “soft swimmers”. Incompressibility and rigidity constraints can be applied in all phases by adopting Eulerian projection approaches commonly used in CFD. The addition of the reference map field to the grid also presents certain benefits when computing level-set interface advection, including a procedure to guarantee mass conservation.

PICS Colloquium: "Data-driven model reduction and multiscale modal decomposition for complex chaotic systems"
2020-10-23 14:00 - 15:00 | PA
Many complex nonequilibrium systems, including turbulent flows, are characterized by chaotic dynamics, a large number of degrees of freedom, and hierarchical, multiscale structure in space and time. In two vignettes, we describe some recent work aimed at developing and applying machine learning and data science tools for systems displaying these characteristics.
The first vignette builds on the idea that while partial differential equations are formally infinite- dimensional, the presence of energy dissipation drives the long-time dynamics onto a finite-dimensional invariant manifold sometimes called an inertial manifold (IM). We describe a data-driven framework to represent chaotic dynamics on this manifold and illustrate it with data from simulations of the Kuramoto-Sivashinsky equation. A hybrid method combining linear and nonlinear (neural-network) dimension reduction transforms between coordinates in the full state space and on the IM. Additional neural networks predict time evolution on the IM; this can be done in either the discrete-time (difference equation) or continuous-time (ordinary differential equation) setting. The formalism accounts for translation invariance and energy conservation, and substantially outperforms linear dimension reduction, reproducing very well key dynamic and statistical features of the attractor.
The second vignette addresses how to represent flow or other fields with multiscale structure. We describe a method, inspired by wavelet analysis, that adaptively decomposes a dataset into an hierarchy of structures (specifically orthogonal basis vectors) localized in scale and space: a “data-driven wavelet decomposition”. This decomposition reflects the inherent structure of the dataset it acts on. In particular, when applied to turbulent flow data, it reveals spatially localized, self-similar, hierarchical structures. It is important emphasize that self-similarity is not built into the analysis, rather, it emerges from the data. This approach is a starting point for the characterization of localized hierarchical turbulent structures that we may think of as the building blocks of turbulence. It will also find application to other systems, such as atmospheres, oceans, biological tissues, active matter and many others, that display multiscale spatiotemporal structure.

SIG Seminar: "Physics-based Animation at Pixar"
2020-10-23 12:00 - 13:00 | PA
Pixar’s films rely heavily on physically simulated effects such as the motion of hairs, cloth, and water, to cite a few. Whereas physical accuracy is of paramount importance in engineering applications, the most important aspects of feature film productions are directability and speed. In this talk, I’ll survey our recent work in this area, including robust hyperelastic materials model for volume simulation and high-performance physically-based sculpting techniques.

BE Seminar: "High-throughput Screening of a Combinatorial CAR Co-stimulatory Domain Library" (Kyle Daniels)
2020-10-22 15:00 - 16:00 | PA
This event will be held virtually on zoom. Check email for zoom link or email ksas@seas.upenn.edu.
CAR T cells—T cells engineered to express a chimeric antigen receptor that redirects their function to a specific antigen—have proven to be an effective therapy for certain B cell cancers, but many issues remain in order to apply CAR T cells to a broader range of cancers. The activity of CAR T cells can be modulated by varying their co-stimulatory domains. Most CARs use co-stimulatory domains from natural proteins such as 41BB or CD28, each of which contains motifs that recruit unique signaling molecules and elicit a corresponding T cell response. One strategy to achieve increased control over T cell function is to engineer synthetic co-stimulatory domains composed of novel combinations of motifs from natural co-stimulatory proteins. We constructed libraries of CARs containing synthetic co-stimulatory domains and screened these library in primary human T cells for the ability to promote proliferation, degranulation, and memory formation. The results of the screens give insights into how signaling motifs dictate cell function and offer clues on how to engineer co-stimulatory domains that promote desired CAR T cell functions.

ESE Seminar: "New Tools for Better Understanding Social Networks"
2020-10-22 11:00 - 12:00 | Zoom – Email ESE for Link jbatter@seas.upenn.edu
We shall examine in our talk concepts and tools for the analysis of social networks. We shall present in particular YouTube and Twitter. For YouTube, we point at shortcomings in ways to measure audience retention and propose new concepts to better quantify desirable properties. We then present a geo-linguistic analysis of Twitter based on daily periodograms and use these to study the evolution of languages as seen in Twitter.

MSE Seminar: "Polymer Informatics: Current Status & Critical Next Steps"
2020-10-22 10:45 - 11:45 | PA
The Materials Genome Initiative (MGI) has heralded a sea change in the philosophy of materials design. In an increasing number of applications, the successful deployment of novel materials has benefited from the use of computational, experimental and informatics methodologies. Here, we describe the role played by computational and experimental data generation and capture, polymer fingerprinting, machine-learning based property prediction models, and algorithms for designing polymers meeting target property requirements. These efforts have culminated in the creation of an online Polymer Informatics platform (https://www.polymergenome.org) to guide ongoing and future polymer discovery and design [1-3]. Challenges that remain will be examined, and systematic steps that may be taken to extend the applicability of such informatics efforts to a wide range of technological domains will be discussed. These include strategies to deal with the data bottleneck, new methods to represent polymer morphology and processing conditions, and the applicability of emerging AI algorithms for materials design.

CBE Seminar: "Engineering Pathways Across Biological Barriers"
2020-10-21 15:00 - 16:00 | Zoom – Email CBE for link
Abstract
Engineering pathways across biological barriers is entering a new era with the rapid advancement of computational resources. My research group focuses on developing multi-scale simulation methods to elucidate the interfacial phenomena associated with biological barriers that play a role in life-threatening diseases, such as Alzheimer’s, cancer, and chronic infections. Our goal is to influence this experimentally-dominated research field by providing mechanistic, structural, and molecular insights into the barrier functions that were computationally unattainable prior to our work. In past few years, we have made breakthroughs in three research domains: elucidated the molecular architecture of the blood-brain barrier and developed strategies to enhance the barrier’s permeability for treatment of the neurodegenerative diseases; developed telodendrimer-based nanocarriers for efficient delivery of approved anticancer drugs for treatment of solid tumors; and designed an online computational platform to screen libraries of small molecules for their permeability across bacterial membranes, and determine their use as antibiotics for treatment of chronic infections. All three research domains have ties with experimental groups to ensure the validity of our research findings. In my talk, I will elaborate on our computational methods, present the key results, and provide a perspective on the long-term research goals of the group.

PICS Alumni Spotlight: "Whelton Miller"
2020-10-21 12:00 - 13:00 | Zoom – Email CIS for link, cherylh@cis.upenn.edu
The PICS alumni Spotlight is an opportunity for students to learn from and network with alumni.

CIS Seminar: "Theoretical Reflections on Quantum Supremacy"
2020-10-20 15:00 - 16:00 | Zoom – Email CIS for link, cherylh@cis.upenn.edu
The recent demonstration of quantum supremacy by Google is a first step towards the era of small to medium scale quantum computers. In this talk I will explain what the experiment accomplished and the theoretical work it is based on, as well as what it did not accomplish and the many theoretical and practical challenges that remain. I will also describe recent breakthroughs in the design of protocols for the testing and benchmarking of quantum computers, a task that has deep computational and philosophical implications. Specifically, this leads to protocols for scalable and verifiable quantum supremacy, certifiable quantum random generation and verification of quantum computation.

MEAM Seminar: "Engineering Solutions for Tough Problems in Trauma: From Occlusion Balloons to Decision-Support"
2020-10-20 10:30 - 12:00 | Zoom – Email MEAM for Link, peterlit@seas.upenn.edu
Acute hemorrhage and hemorrhagic shock result in approximately 60,000 annual deaths in the United States. The vast majority of these deaths are in severely injured patients, but experts in trauma care believe many of these deaths can actually be prevented. Efforts focused on injury prevention and pre-emptive intervention have produced some improvements in survival. However, for those who sustain severe injuries, a range of engineering solutions could mitigate the risk of death from hemorrhage.
In this talk, I will review the current epidemiology of acute hemorrhage and our understanding of hemorrhagic shock. I will then explore several recent innovations designed to stop acute hemorrhage and guide teams in real-time management: balloon aortic occlusion and hemorrhage decision support. These innovations will be discussed in the context of the regulatory hurdles that must be navigated to bring these and other similar innovations to the bedside.

PSOC Webinar: “A three dimensional extracellular matrix supports organoid phenotype by reducing cortical actin tension to maintain endoplasmic reticulum function” (FuiBoon Kai)
2020-10-19 12:00 - 13:00 | PA
“A three dimensional extracellular matrix supports organoid phenotype by reducing cortical actin tension to maintain endoplasmic reticulum function”
Physical Sciences in Oncology Center PSOC@Penn
Fall 2020 Webinar Series Mondays @ Noon (EST)
For webinar links, please contact manu@seas.upenn.edu

PICS: "C++ Workshop"
2020-10-17 10:00 - 16:00 | Zoom – Email CIS for link, cherylh@cis.upenn.edu
The Penn Institute for Computational Science (PICS) is proud to host a C++ workshop on Saturday, October 17 from 10:00am – 3:30pm via Zoom. This one day workshop will teach students the basic skills needed to program in C++. We ask that anyone who attends has at least one year of prior programming experience in any language.
You must click here and RSVP to attend this event. The class will be capped at 30 people. Students enrolled in the PICS certificate program will receive priority enrollment.

SIG Seminar: "Uncertainty-Driven Geometry Reconstruction and Understanding"
2020-10-16 10:00 - 11:00 | Zoom – Email CIS for link, cherylh@cis.upenn.edu
Choosing suitable data representations is one of the most critical topics when installing machine learning on 3D data. This talk discusses several recent works on utilizing uncertainty as a lens to develop suitable data representations and formulations for geometry reconstruction and understanding. We will discuss both theoretical results and applications in multi-scan registration, pose estimation, and scene understanding.

MSE Seminar: "The interplay between phase transformation and deformation mechanisms in multicomponent metal alloys"
2020-10-15 10:45 - 11:45 | PA
Phase prediction in multicomponent alloys remains one of the most fundamental challenges. Navigating the vast compositional space of these alloys requires a predictive capability to efficiently guide alloy discovery and microstructure design. Recently, Dr. Ghazisaeidi has developed a Multicell Monte Carlo (MC)^2 method, based on first-principles calculations, to study phase formation in multicomponent alloys. This method is particularly powerful when applied to multicomponent systems, for which phase diagrams do not exist. First, she introduce the (MC)^2 method and present its successful prediction of the stable phases of known binary systems. Next, she will present the application of (MC)^2 to “high entropy” alloys and discuss the effect of emerging stable phases on deformation mechanisms and consequently the overall mechanical behavior.

CBE Seminar: "Fuels and Chemicals Decarbonized"
2020-10-14 15:00 - 16:00 | Zoom – Email CBE for link
Abstract
Fuels based on hydrocarbons, taken together with the processing of raw hydrocarbons into chemicals, account for about half of the United States’ CO2 emissions. Efforts to lessen the CO2 footprint of these activities have the potential to make a significant impact on total greenhouse emissions. We focus on taking CO2 and – with energy supplied by low carbon/renewable electricity – turning it into fuels and fuel additives such as methane, ethanol, and propanol. I will update on progress in the energy efficiency and intensity (activity) of these processes. We also seek to advance the electrosynthesis of renewable chemicals such as ethylene and – more recently on the anodic side – ethylene glycol and ethylene oxide.

ESE Seminar: "Data-driven Decision Making and Estimation in CPS/IoT Networks"
2020-10-13 12:00 - 13:00 | Zoom – Email ESE for Link jbatter@seas.upenn.edu
The modern instantiation of a sensor network is a cyberphysical (CPS) system where CPS subsystems can be interconnected by a shared communication network of limited bandwidth. A common problem in CPS networks is the sensing and communication of spatio-temporal signals. However, there are fundamental differences between estimation (sensing) and communication. For example, the type of signal one would design to optimize sensing is very different from that for optimized communication. In this talk, we explore some of these differences and discuss how joint communication and sensing should occur in different problem settings. In particular, we examine problems where multiple sensors make observations and must share the communication medium to transmit these signals to a fusion center that will endeavor to perform remote estimation all of the sensed signals. A new class of remote estimation problems, where the communication resources are allocated dynamically based on the observations at the sensors, rather than purely on their statistical description is examined. We address the optimal design of a collision avoidance policy by selecting the most informative sensor to transmit at a time. First, we will establish person-by-person optimal policies for the scheduling of sensors making Gaussian observations. Then, we will show how our theoretical results can be applied to design scheduling policies where the joint probability density of the observations is unknown using machine learning techniques. We will extend our results to the case when the scheduler uses an energy harvesting battery as well as to the case of purely decentralized decision making. Time permitting, applications to the case of microbial decision making in quorum sensing networks will be discussed.

MEAM Seminar: "Real-Time Reduced Order Modeling using Time-Dependent Basis: Applications in Turbulent Flows and Combustion"
2020-10-13 10:30 - 12:00 | Zoom – Email MEAM for Link, peterlit@seas.upenn.edu
The question of “what set of basis functions should be used?” is of fundamental importance to scientific computing. The performance of different choices of basis is assessed primarily by the rate of convergence and robustness. However, when the one-dimensional basis is extended to higher dimensions d, the computational complexity increases exponentially with respect to d. This fundamental challenge has been dubbed the curse of dimensionality and it is one of the greatest impediments to solving many important problems in science and engineering. One of the most promising approaches to beat the curse of dimensionality is exploiting correlations of the solution between various dimensions. In this work, a framework for extracting and exploiting correlated structures is presented. The correlated structures take the form a set of time-dependent basis. This framework is based on a variational principle whose optimality condition leads to closed form evolution equations for a set time-dependent bases. The bases are directly extracted from the model (e.g. partial differential equations) or from the streaming data. Several applications will be presented including computing sensitivities in chaotic dynamical systems and real-time reduced order modeling of turbulent reactive flows.

PSOC Webinar: "Manipulating Macrophage Signaling" (Meghan Morrissey)
2020-10-12 12:00 - 13:00 | PA
“Manipulating Macrophage Signaling – How macrophages measure ‘Eat me’ and ‘Don’t eat me’ signals”
Physical Sciences in Oncology Center PSOC@Penn
Fall 2020 Webinar Series Mondays @ Noon (EST)
For webinar links, please contact manu@seas.upenn.edu

PICS Seminar: "Scaling down the laws of thermodynamics"
2020-10-09 14:00 - 15:00 | Zoom – Email CIS for link, cherylh@cis.upenn.edu
Abstract: Thermodynamics provides a robust conceptual framework and set of laws that govern the exchange of energy and matter. Although these laws were originally articulated for macroscopic objects, nanoscale systems also exhibit “thermodynamic-like” behavior – for instance, biomolecular motors convert chemical fuel into mechanical work, and single molecules exhibit hysteresis when manipulated using optical tweezers. To what extent can the laws of thermodynamics be scaled down to apply to individual microscopic systems, and what new features emerge at the nanoscale? I will describe some of the challenges and recent progress – both theoretical and experimental – associated with addressing these questions. Along the way, my talk will touch on non-equilibrium fluctuations, “violations” of the second law, the thermodynamic arrow of time, nanoscale feedback control, strong system-environment coupling, and quantum thermodynamics.

SIG Seminar: “A History of Crowd Simulation and Rendering at Pixar”
2020-10-09 10:00 - 11:00 | PA
This talk will cover how Pixar’s crowds pipeline evolved from “A Bug’s Life” to “Onward”, and how the studio’s artists and engineers refined and re-invented their tools over the years to create memorable animated crowd scenes. We’ll cover the progression from finite state machine control, to agent based crowd simulation, to sketch based workflows, using case studies from Pixar’s feature films.

BE Seminar: "Imaging and sequencing single cells" (Aaron Streets)
2020-10-08 14:00 - 15:00 | PA
This event will be held virtually on zoom. Check your email for the link and passcode or contact ksas@seas.upenn.edu.
Recent advances in microfluidics and high-throughput sequencing technology have enabled rapid profiling of genomic material in single cells. Valve- and droplet-based microfluidic platforms can precisely and efficiently manipulate, sort, and process cells to generate indexed sequencing libraries, allowing for high-throughput single-cell analysis of the genome, transcriptome, proteome, and epigenome. Such technology has been instrumental in the global effort to create a human cell atlas, with the ambitious goal of identifying and cataloging all human cell types and cell states in health and disease. However, not all cell phenotypes are directly encoded in the genome and high-throughput sequencing cannot probe the full space of cellular identity. Therefore, microscopy remains one of the most powerful and versatile tools for characterizing cells. Fluorescent imaging and quantitative non-linear optical imaging can reveal morphological characteristics, protein localization, chromatin organization, and chemical composition in single cells. Both single-cell genomics and microscopy can uncover heterogeneity in cellular populations that would otherwise be obscured in ensemble measurement. In this talk, I will discuss a suite of new microfluidic platforms for coupling genomic measurements and optical measurements of the same single cell, and some novel computational approaches to grapple with these new datasets. With a combination of new hardware and software, our goal is to converge on a quantitative and comprehensive understanding of cellular identity.

MSE Seminar: "Characterization of Complex Eutectic Microstructures"
2020-10-08 10:45 - 11:45 | PA
Eutectic phase transitions play an important role in many engineering materials, from cast iron to electronic solder. Regular binary eutectics are relatively straightforward and generally well understood, but the additional degree of freedom in three-component alloys introduces a far greater level of complexity, as three solid phases can form simultaneously from the melt. These ternary eutectic structures show promise for creating materials with unique optical and electronic properties, and are also useful for understanding the factors that control multi-phase, multi-component solidification more generally. The fundamentals of higher-order eutectics will be explained, before describing recent experimental and simulation work primarily on the model system of Al-Ag-Cu. By directional solidification at relatively low velocities and thermal gradients, ternary microstructures with varying degrees of alignment were produced and studied. These microstructures will be discussed both qualitatively and quantitatively, along with the role of diffusion, interfacial energy and crystal structure in their formation.

CIS Seminar:"Language, Brain, and Computation"
2020-10-06 15:00 - 16:00 | Zoom – Email CIS for link, cherylh@cis.upenn.edu
How does the brain beget the mind? How do molecules, cells and synapses effect reasoning, intelligence, language? Despite dazzling progress in experimental neuroscience, as well as in cognitive science at the other extreme of scale, we do not seem to be making progress in the overarching question — the gap is huge and a completely new approach seems to be required. As Richard Axel recently put it: “We don’t have a logic for the transformation of neural activity into thought […].”
What kind of formal system would qualify as this “logic”?
I will introduce the Assembly Calculus, a computational system whose basic data structure is the assembly — assemblies are large populations of neurons representing concepts, words, thoughts, etc. –, and which is informed by recent progress in understanding how language happens in the brain.

ESE Grace Hopper Lecture: "Emerging Non-Volatile Ferroelectric Memory"
2020-10-06 11:00 - 12:00 | Zoom – Email ESE for Link jbatter@seas.upenn.edu
Abstract
The last decade has seen a remarkable shift in usage and value of semiconductor memory technologies. These changes are driven by the elevation of four particular target applications –(1) mobile multi-media applications, (2) explosive growth in the sheer volume of data that is being created and stored, (3) emphasis from the individual components to the configurability in high-volume subsystems and (4) applications in brain inspired artificial intelligence systems.
The dominating memory technologies in the industry have been SRAM, DRAM (volatile) and NAND flash (non-volatile). Storage class memory (SCM) describes a device category that combines the benefits of solid-state memory with the archival capabilities and low cost per bit of conventional hard disk magnetic storage. In the past decade, significant focus has been put on the emerging memory technologies that include: MRAM (Magnetic RAM), STTRAM (Spin-Transfer Torque RAM), FeRAM (Ferroelectric RAM), PCRAM (Phase Change RAM), RRAM (Resistive RAM) and Memristor.
The invention of ferroelectricity in doped hafnium based oxides (HfZrO2, doped HfO2) has attracted tremendous interest in realizing HfO 2 based devices. They have large remnant polarization of up to 45 μC cm −2 , and their coercive field (≈1–2 MV cm −1 ) is larger than conventional ferroelectric films by approximately one order of magnitude. Furthermore, they can be extremely thin (<10 nm) and have a large bandgap (>5 eV). The primary devices aimed in these applications are ferroelectric field effect transistors (FeFETs) and ferroelectric tunnel junctions (FTJs). In FeFETs, the conventional logic gate dielectric is replaced with a ferroelectric material that remembers the electric field to which it had been exposed resulting in the threshold voltage of two stable binary states similar to the way it is done in a flash memory cell.
We are aiming at developing a fabrication platform that will allow fabrication of n and p channel FeFETs and FTJ based circuits using standard CMOS process on 150 mm wafers in a university environment. We observe FeFETs exhibiting charge trapping and polarization induced memory window. The process developed for fabricating 1T1R FTJ array integrated with NMOS will be described. FTJs are promising candidates for synaptic weight elements in neural network hardware because of their nonvolatile multilevel memory effect. The talk will provide an overview of advances made in various memory technologies with their future trends.

MEAM Seminar: "Operator Inference: Bridging Model Reduction and Scientific Machine Learning"
2020-10-06 10:30 - 12:00 | Zoom – Email MEAM for Link, peterlit@seas.upenn.edu
Model reduction methods have grown from the computational science community, with a focus on reducing high-dimensional models that arise from physics-based modeling, whereas machine learning has grown from the computer science community, with a focus on creating expressive models from black-box data streams. Yet recent years have seen an increased blending of the two perspectives and a recognition of the associated opportunities. This talk presents our work in operator inference, where we learn effective reduced-order operators directly from data. The physical governing equations define the form of the model we should seek to learn. Thus, rather than learn a generic approximation with weak enforcement of the physics, we learn low-dimensional operators whose structure is defined by the physics. This perspective provides new opportunities to learn from data through the lens of physics-based models and contributes to the foundations of Scientific Machine Learning, yielding a new class of flexible data-driven methods that support high-consequence decision-making under uncertainty for physical systems.
