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Jean Anne C. Incorvia - IEEE Xplore Author Profile
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Jean Anne C. Incorvia

Also published under: J. A. Incorvia, Jean Anne Incorvia

Publications
28
Citations
212
Publications by Year
20172024
Co-Authors:
Show All Co-Authors (94)
Image of author Jean Anne C. Incorvia

Jean Anne C. Incorvia

Also published under: J. A. Incorvia, Jean Anne Incorvia

Affiliation

Department of Electrical and Computer Engineering
University of Texas at Austin
Austin, TX, USA

Publication Topics

Biography

Jean Anne C. Incorvia (incorvia@austin.utexas.edu) is with the Department of Electrical and Computer Engineering, the University of Texas at Austin, Austin, TX 78705-2241 USA. They are a Senior Member of IEEE.(Based on document published on 22 October 2024).
Publications
28
Citations
212
Publications by Year
20172024
Co-Authors:
Show All Co-Authors (94)
Author's Published Works

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Monolayer hBN RRAM with High DC Endurance and Low Operation Voltages using an Oxidized Top Electrode

Hui-Ping Chang;Deji Akinwande;Jean Anne C. Incorvia

2024 Device Research Conference (DRC)
Year: 2024 | Conference Paper |
The rapid evolution of artificial intelligence technologies has escalated the need for compact, high-density memory solutions [1] . Monolayer hexagonal boron nitride resistive random access memory (ML hBN RRAM) emerges as a particularly promising candidate due to its simple, ultra-thin metal-insulator-metal structure. However, these atomristor devices usually wear out quickly, with a maximum of 10...Show More

Monolayer hBN RRAM with High DC Endurance and Low Operation Voltages using an Oxidized Top Electrode

Hui-Ping Chang;Deji Akinwande;Jean Anne C. Incorvia

Year: 2024 | Conference Paper |

Device Codesign using Reinforcement Learning

Suma G. Cardwell;Karan Patel;Catherine D. Schuman;J. Darby Smith;Jaesuk Kwon;Andrew Maicke;Jared Arzate;Jean Anne C. Incorvia

2024 IEEE International Symposium on Circuits and Systems (ISCAS)
Year: 2024 | Conference Paper |
We demonstrate device codesign using reinforcement learning for probabilistic computing applications. We use a spin orbit torque magnetic tunnel junction model (SOT-MTJ) as the device exemplar. We leverage reinforcement learning (RL) to vary key device and material properties of the SOT-MTJ device for stochastic operation. Our RL method generated different candidate devices capable of generating s...Show More

Device Codesign using Reinforcement Learning

Suma G. Cardwell;Karan Patel;Catherine D. Schuman;J. Darby Smith;Jaesuk Kwon;Andrew Maicke;Jared Arzate;Jean Anne C. Incorvia

Year: 2024 | Conference Paper |

Domain Wall-Magnetic Tunnel Junction Analog Content Addressable Memory Using Current and Projected Data

Harrison Jin;Hanqing Zhu;Keren Zhu;Thomas Leonard;Jaesuk Kwon;Mahshid Alamdar;Kwangseok Kim;Jungsik Park;Naoki Hase;David Z. Pan;Jean Anne C. Incorvia

IEEE Transactions on Nanotechnology
Year: 2024 | Volume: 23 | Journal Article |
Cited by: Papers (1)
With the rise in in-memory computing architectures to reduce the compute-memory bottleneck, a new bottleneck is present between analog and digital conversion. Analog content-addressable memories (ACAM) are being recently studied for in-memory computing to efficiently convert between analog and digital signals. Magnetic memory elements such as magnetic tunnel junctions (MTJs) could be useful for AC...Show More

Domain Wall-Magnetic Tunnel Junction Analog Content Addressable Memory Using Current and Projected Data

Harrison Jin;Hanqing Zhu;Keren Zhu;Thomas Leonard;Jaesuk Kwon;Mahshid Alamdar;Kwangseok Kim;Jungsik Park;Naoki Hase;David Z. Pan;Jean Anne C. Incorvia

Year: 2024 | Volume: 23 | Journal Article |

Radiation Response of Domain-Wall Magnetic Tunnel Junction Logic Devices

Christopher H. Bennett;T. Patrick Xiao;Thomas Leonard;Xun Zhan;Raluca Gearba-Dolocan;Joshua Young;Gyorgy Vizkelethy;Ed Bielejec;David Hughart;Matthew Marinella;Jean Anne Incorvia

IEEE Transactions on Nuclear Science
Year: 2024 | Volume: 71, Issue: 4 | Journal Article |
Cited by: Papers (1)
Domain-wall magnetic tunnel junctions (MTJs) are a new spintronic device family that may be exploited in resilient edge logic processors or neuromorphic edge accelerators in the future. Here, domain-wall MTJ logic devices were exposed to large total ionizing doses (TIDs), heavy ion displacement damage, or both. The parts demonstrated complete resilience to the ionizing radiation, but ion-irradiate...Show More

Radiation Response of Domain-Wall Magnetic Tunnel Junction Logic Devices

Christopher H. Bennett;T. Patrick Xiao;Thomas Leonard;Xun Zhan;Raluca Gearba-Dolocan;Joshua Young;Gyorgy Vizkelethy;Ed Bielejec;David Hughart;Matthew Marinella;Jean Anne Incorvia

Year: 2024 | Volume: 71, Issue: 4 | Journal Article |

Fabrication of Domain Wall based Magnetic Tunnel Junction Devices with Intrinsic Neuromorphic Functionality

Thomas Leonard;Samuel Liu;Harrison Jin;Joseph S. Friedman;Christopher Bennett;Jean Anne Incorvia

2023 IEEE 34th Magnetic Recording Conference (TMRC)
Year: 2023 | Conference Paper |
We summarize our progress towards monolithic and analog neuromorphic computing utilizing domain wall-magnetic tunnel junction (DW-MTJ) devices. We have previously shown device performance for binary logic DW-MTJ devices. Here, we expand on that work by demonstrating neuromorphic functionality using shape-dependent tunability. We measure multi-weight synapses and stochastic neurons monolithically f...Show More

Fabrication of Domain Wall based Magnetic Tunnel Junction Devices with Intrinsic Neuromorphic Functionality

Thomas Leonard;Samuel Liu;Harrison Jin;Joseph S. Friedman;Christopher Bennett;Jean Anne Incorvia

Year: 2023 | Conference Paper |

Analysis of Skyrmion Shuffling Chamber Stochasticity for Neuromorphic Computing Applications

Zulfidin Khodzhaev;Emrah Turgut;Jean Anne C. Incorvia

IEEE Magnetics Letters
Year: 2023 | Volume: 14 | Journal Article |
In this study, micromagnetic simulations of a magnetic skyrmion reshuffling chamber for probabilistic computing applications are performed. The skyrmion shuffling chamber is modeled with a custom current density masking technique to capture current density variation, grain boundary variations, and anisotropy changes. The results show that the skyrmion oscillatory dynamics contribute to the system'...Show More

Analysis of Skyrmion Shuffling Chamber Stochasticity for Neuromorphic Computing Applications

Zulfidin Khodzhaev;Emrah Turgut;Jean Anne C. Incorvia

Year: 2023 | Volume: 14 | Journal Article |

Parallel Matrix Multiplication Using Voltage-Controlled Magnetic Anisotropy Domain Wall Logic

Nicholas Zogbi;Samuel Liu;Christopher H. Bennett;Sapan Agarwal;Matthew J. Marinella;Jean Anne C. Incorvia;T. Patrick Xiao

IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Year: 2023 | Volume: 9, Issue: 1 | Journal Article |
Cited by: Papers (5)
The domain wall-magnetic tunnel junction (DW-MTJ) is a versatile device that can simultaneously store data and perform computations. These three-terminal devices are promising for digital logic due to their nonvolatility, low-energy operation, and radiation hardness. Here, we augment the DW-MTJ logic gate with voltage-controlled magnetic anisotropy (VCMA) to improve the reliability of logical conc...Show More

Parallel Matrix Multiplication Using Voltage-Controlled Magnetic Anisotropy Domain Wall Logic

Nicholas Zogbi;Samuel Liu;Christopher H. Bennett;Sapan Agarwal;Matthew J. Marinella;Jean Anne C. Incorvia;T. Patrick Xiao

Year: 2023 | Volume: 9, Issue: 1 | Journal Article |

Probabilistic Neural Circuits leveraging AI-Enhanced Codesign for Random Number Generation

Suma G. Cardwell;Catherine D. Schuman;J. Darby Smith;Karan Patel;Jaesuk Kwon;Samuel Liu;Christopher Allemang;Shashank Misra;Jean Anne Incorvia;James B. Aimone

2022 IEEE International Conference on Rebooting Computing (ICRC)
Year: 2022 | Conference Paper |
Stochasticity is ubiquitous in the world around us. However, our predominant computing paradigm is deterministic. Random number generation (RNG) can be a computationally inefficient operation in this system especially for larger workloads. Our work leverages the underlying physics of emerging devices to develop probabilistic neural circuits for RNGs from a given distribution. However, codesign for...Show More

Probabilistic Neural Circuits leveraging AI-Enhanced Codesign for Random Number Generation

Suma G. Cardwell;Catherine D. Schuman;J. Darby Smith;Karan Patel;Jaesuk Kwon;Samuel Liu;Christopher Allemang;Shashank Misra;Jean Anne Incorvia;James B. Aimone

Year: 2022 | Conference Paper |

Random Bitstream Generation Using Voltage-Controlled Magnetic Anisotropy and Spin Orbit Torque Magnetic Tunnel Junctions

Samuel Liu;Jaesuk Kwon;Paul W. Bessler;Suma G. Cardwell;Catherine Schuman;J. Darby Smith;James B. Aimone;Shashank Misra;Jean Anne C. Incorvia

IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Year: 2022 | Volume: 8, Issue: 2 | Journal Article |
Cited by: Papers (8)
Probabilistic computing using random number generators (RNGs) can leverage the inherent stochasticity of nanodevices for system-level benefits. Device candidates for this application need to produce highly random “coinflips” while also having tunable biasing of the coin. The magnetic tunnel junction (MTJ) has been studied as an RNG due to its thermally-driven magnetization dynamics, often using sp...Show More

Random Bitstream Generation Using Voltage-Controlled Magnetic Anisotropy and Spin Orbit Torque Magnetic Tunnel Junctions

Samuel Liu;Jaesuk Kwon;Paul W. Bessler;Suma G. Cardwell;Catherine Schuman;J. Darby Smith;James B. Aimone;Shashank Misra;Jean Anne C. Incorvia

Year: 2022 | Volume: 8, Issue: 2 | Journal Article |

Purely Spintronic Leaky Integrate-and-Fire Neurons

Wesley H. Brigner;Naimul Hassan;Xuan Hu;Christopher H. Bennett;Felipe Garcia-Sanchez;Matthew J. Marinella;Jean Anne C. Incorvia;Joseph S. Friedman

2022 IEEE International Symposium on Circuits and Systems (ISCAS)
Year: 2022 | Conference Paper |
Cited by: Papers (3)
Neuromorphic computing promises revolutionary improvements over conventional systems for applications that process unstructured information. To fully realize this potential, neuromorphic systems should exploit the biomimetic behavior of emerging nanodevices. In particular, exceptional opportunities are provided by the non-volatility and analog capabilities of spintronic devices. While spintronic d...Show More

Purely Spintronic Leaky Integrate-and-Fire Neurons

Wesley H. Brigner;Naimul Hassan;Xuan Hu;Christopher H. Bennett;Felipe Garcia-Sanchez;Matthew J. Marinella;Jean Anne C. Incorvia;Joseph S. Friedman

Year: 2022 | Conference Paper |

Intrinsic Lateral Inhibition Facilitates Winner-Take-All in Domain Wall Racetrack Arrays for Neuromorphic Computing

Can Cui;Otitoaleke G. Akinola;Naimul Hassan;Christopher H. Bennett;Matthew J. Marinella;Joseph S. Friedman;Jean Anne C. Incorvia

2022 IEEE International Symposium on Circuits and Systems (ISCAS)
Year: 2022 | Conference Paper |
Cited by: Papers (2)
Neuromorphic computing is a promising candidate for beyond-von Neumann computer architectures, featuring low power consumption and high parallelism. Lateral inhibition and winner-take-all (WTA) features play a crucial role in neuronal competition of the nervous system as well as neuromorphic hardwares. The domain wall - magnetic tunnel junction (DWMTJ) neuron is an emerging spintronic artificial n...Show More

Intrinsic Lateral Inhibition Facilitates Winner-Take-All in Domain Wall Racetrack Arrays for Neuromorphic Computing

Can Cui;Otitoaleke G. Akinola;Naimul Hassan;Christopher H. Bennett;Matthew J. Marinella;Joseph S. Friedman;Jean Anne C. Incorvia

Year: 2022 | Conference Paper |

Logical and Physical Reversibility of Conservative Skyrmion Logic

Xuan Hu;Benjamin W. Walker;Felipe García-Sánchez;Alexander J. Edwards;Peng Zhou;Jean Anne C. Incorvia;Alexandru Paler;Michael P. Frank;Joseph S. Friedman

IEEE Magnetics Letters
Year: 2022 | Volume: 13 | Journal Article |
Cited by: Papers (2)
Magnetic skyrmions are nanoscale whirls of magnetism that can be propagated with electrical currents. The repulsion between skyrmions inspires their use for reversible computing based on the elastic billiard ball collisions proposed for conservative logic in 1982. In this letter, we evaluate the logical and physical reversibility of this skyrmion logic paradigm, as well as the limitations that mus...Show More

Logical and Physical Reversibility of Conservative Skyrmion Logic

Xuan Hu;Benjamin W. Walker;Felipe García-Sánchez;Alexander J. Edwards;Peng Zhou;Jean Anne C. Incorvia;Alexandru Paler;Michael P. Frank;Joseph S. Friedman

Year: 2022 | Volume: 13 | Journal Article |

Domain Wall Leaky Integrate-and-Fire Neurons With Shape-Based Configurable Activation Functions

Wesley H. Brigner;Naimul Hassan;Xuan Hu;Christopher H. Bennett;Felipe Garcia-Sanchez;Can Cui;Alvaro Velasquez;Matthew J. Marinella;Jean Anne C. Incorvia;Joseph S. Friedman

IEEE Transactions on Electron Devices
Year: 2022 | Volume: 69, Issue: 5 | Journal Article |
Cited by: Papers (11)
CMOS devices display volatile characteristics and are not well suited for analog applications such as neuromorphic computing. Spintronic devices, on the other hand, exhibit both non-volatile and analog features, which are well suited to neuromorphic computing. Consequently, these novel devices are at the forefront of beyond-CMOS artificial intelligence applications. However, a large quantity of th...Show More

Domain Wall Leaky Integrate-and-Fire Neurons With Shape-Based Configurable Activation Functions

Wesley H. Brigner;Naimul Hassan;Xuan Hu;Christopher H. Bennett;Felipe Garcia-Sanchez;Can Cui;Alvaro Velasquez;Matthew J. Marinella;Jean Anne C. Incorvia;Joseph S. Friedman

Year: 2022 | Volume: 69, Issue: 5 | Journal Article |

Controllable Reset Behavior in Domain Wall–Magnetic Tunnel Junction Artificial Neurons for Task-Adaptable Computation

Samuel Liu;Christopher H. Bennett;Joseph S. Friedman;Matthew J. Marinella;David Paydarfar;Jean Anne C. Incorvia

IEEE Magnetics Letters
Year: 2021 | Volume: 12 | Journal Article |
Cited by: Papers (8)
Neuromorphic computing with spintronic devices has been of interest due to the limitations of CMOS-driven von Neumann computing. Domain wall-magnetic tunnel junction (DW-MTJ) devices have been shown to be able to intrinsically capture biological neuron behavior. Edgy-relaxed behavior, where a frequently firing neuron experiences a lower action potential threshold, may provide additional artificial...Show More

Controllable Reset Behavior in Domain Wall–Magnetic Tunnel Junction Artificial Neurons for Task-Adaptable Computation

Samuel Liu;Christopher H. Bennett;Joseph S. Friedman;Matthew J. Marinella;David Paydarfar;Jean Anne C. Incorvia

Year: 2021 | Volume: 12 | Journal Article |

Irradiation Effects on Perpendicular Anisotropy Spin–Orbit Torque Magnetic Tunnel Junctions

Mahshid Alamdar;Liang Juan Chang;Karalee Jarvis;Paul Kotula;Can Cui;Raluca Gearba-Dolocan;Yihan Liu;Enrique Antunano;Jack E. Manuel;Gyorgy Vizkelethy;Lin Xue;Robin Jacobs-Gedrim;Christopher H. Bennett;T. Patrick Xiao;David Hughart;Edward Bielejec;Matthew J. Marinella;Jean Anne C. Incorvia

IEEE Transactions on Nuclear Science
Year: 2021 | Volume: 68, Issue: 5 | Journal Article |
Cited by: Papers (15)
We study the impact of irradiation on magnetic tunnel junction (MTJ) films with perpendicular magnetic anisotropy (PMA) and spin-orbit torque (SOT) switching using magneto-optical Kerr effect and transmission electron microscopy. Our results show that the thin-film stack is robust to gamma ionizing dose up to 1 Mrad(Si) and Ta1+ ion irradiation fluences up to 1012 ions/cm2, showing SOT PMA MTJs ar...Show More

Irradiation Effects on Perpendicular Anisotropy Spin–Orbit Torque Magnetic Tunnel Junctions

Mahshid Alamdar;Liang Juan Chang;Karalee Jarvis;Paul Kotula;Can Cui;Raluca Gearba-Dolocan;Yihan Liu;Enrique Antunano;Jack E. Manuel;Gyorgy Vizkelethy;Lin Xue;Robin Jacobs-Gedrim;Christopher H. Bennett;T. Patrick Xiao;David Hughart;Edward Bielejec;Matthew J. Marinella;Jean Anne C. Incorvia

Year: 2021 | Volume: 68, Issue: 5 | Journal Article |

Hybrid Pass Transistor Logic With Ambipolar Transistors

Xuan Hu;Amy S. Abraham;Jean Anne C. Incorvia;Joseph S. Friedman

IEEE Transactions on Circuits and Systems I: Regular Papers
Year: 2021 | Volume: 68, Issue: 1 | Journal Article |
Cited by: Papers (7)
The pass transistor logic (PTL) family enables compact circuits to reduce area and power consumption, but inter-stage inverters are required for signal integrity and complementary signals. Similarly, dual-gate ambipolar field-effect transistors are exceptionally logically expressive and provide a single-transistor XNOR operation, but numerous inverters are required to provide complementary signals...Show More

Hybrid Pass Transistor Logic With Ambipolar Transistors

Xuan Hu;Amy S. Abraham;Jean Anne C. Incorvia;Joseph S. Friedman

Year: 2021 | Volume: 68, Issue: 1 | Journal Article |

CMOS-Free Magnetic Domain Wall Leaky Integrate-and-Fire Neurons with Intrinsic Lateral Inhibition

Naimul Hassan;Wesley H. Brigner;Xuan Hu;Otitoaleke G. Akinola;Christopher H. Bennett;Matthew J. Marinella;Felipe Garcia-Sanchez;Jean Anne C. Incorvia;Joseph S. Friedman

2020 IEEE International Symposium on Circuits and Systems (ISCAS)
Year: 2020 | Conference Paper |
Spintronic devices, especially those based on motion of a domain wall (DW) through a ferromagnetic track, have received a significant amount of interest in the field of neuromorphic computing because of their non-volatility and intrinsic current integration capabilities. Many spintronic neurons using this technology have already been proposed, but they also require external circuitry or additional...Show More

CMOS-Free Magnetic Domain Wall Leaky Integrate-and-Fire Neurons with Intrinsic Lateral Inhibition

Naimul Hassan;Wesley H. Brigner;Xuan Hu;Otitoaleke G. Akinola;Christopher H. Bennett;Matthew J. Marinella;Felipe Garcia-Sanchez;Jean Anne C. Incorvia;Joseph S. Friedman

Year: 2020 | Conference Paper |

Plasticity-Enhanced Domain-Wall MTJ Neural Networks for Energy-Efficient Online Learning

Christopher H. Bennett;T. Patrick Xiao;Can Cui;Naimul Hassan;Otitoaleke G. Akinola;Jean Anne C. Incorvia;Alvaro Velasquez;Joseph S. Friedman;Matthew J. Marinella

2020 IEEE International Symposium on Circuits and Systems (ISCAS)
Year: 2020 | Conference Paper |
Cited by: Papers (2)
Machine learning implements backpropagation via abundant training samples. We demonstrate a multi-stage learning system realized by a promising non-volatile memory device, the domain-wall magnetic tunnel junction (DW-MTJ). The system consists of unsupervised (clustering) as well as supervised sub-systems, and generalizes quickly (with few samples). We demonstrate interactions between physical prop...Show More

Plasticity-Enhanced Domain-Wall MTJ Neural Networks for Energy-Efficient Online Learning

Christopher H. Bennett;T. Patrick Xiao;Can Cui;Naimul Hassan;Otitoaleke G. Akinola;Jean Anne C. Incorvia;Alvaro Velasquez;Joseph S. Friedman;Matthew J. Marinella

Year: 2020 | Conference Paper |

Process Variation Model and Analysis for Domain Wall-Magnetic Tunnel Junction Logic

Xuan Hu;Alexander J. Edwards;T. Patrick Xiao;Christopher H. Bennett;Jean Anne C. Incorvia;Matthew J. Marinella;Joseph S. Friedman

2020 IEEE International Symposium on Circuits and Systems (ISCAS)
Year: 2020 | Conference Paper |
Cited by: Papers (1)
The domain wall-magnetic tunnel junction (DW-MTJ) is a spintronic device that enables efficient logic circuit design because of its low energy consumption, small size, and non-volatility. Furthermore, the DW-MTJ is one of the few spintronic devices for which a direct cascading mechanism is experimentally demonstrated without any extra buffers; this enables potential design and fabrication of a lar...Show More

Process Variation Model and Analysis for Domain Wall-Magnetic Tunnel Junction Logic

Xuan Hu;Alexander J. Edwards;T. Patrick Xiao;Christopher H. Bennett;Jean Anne C. Incorvia;Matthew J. Marinella;Joseph S. Friedman

Year: 2020 | Conference Paper |

Energy and Performance Benchmarking of a Domain Wall-Magnetic Tunnel Junction Multibit Adder

T. Patrick Xiao;Christopher H. Bennett;Xuan Hu;Ben Feinberg;Robin Jacobs-Gedrim;Sapan Agarwal;John S. Brunhaver;Joseph S. Friedman;Jean Anne C. Incorvia;Matthew J. Marinella

IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Year: 2019 | Volume: 5, Issue: 2 | Journal Article |
Cited by: Papers (22)
The domain-wall (DW)-magnetic tunnel junction (MTJ) device implements universal Boolean logic in a manner that is naturally compact and cascadable. However, an evaluation of the energy efficiency of this emerging technology for standard logic applications is still lacking. In this article, we use a previously developed compact model to construct and benchmark a 32-bit adder entirely from DW-MTJ de...Show More

Energy and Performance Benchmarking of a Domain Wall-Magnetic Tunnel Junction Multibit Adder

T. Patrick Xiao;Christopher H. Bennett;Xuan Hu;Ben Feinberg;Robin Jacobs-Gedrim;Sapan Agarwal;John S. Brunhaver;Joseph S. Friedman;Jean Anne C. Incorvia;Matthew J. Marinella

Year: 2019 | Volume: 5, Issue: 2 | Journal Article |

Shape-Based Magnetic Domain Wall Drift for an Artificial Spintronic Leaky Integrate-and-Fire Neuron

Wesley H. Brigner;Naimul Hassan;Lucian Jiang-Wei;Xuan Hu;Diptish Saha;Christopher H. Bennett;Matthew J. Marinella;Jean Anne C. Incorvia;Felipe Garcia-Sanchez;Joseph S. Friedman

IEEE Transactions on Electron Devices
Year: 2019 | Volume: 66, Issue: 11 | Journal Article |
Cited by: Papers (46)
Spintronic devices based on domain wall (DW) motion through ferromagnetic nanowire tracks have received great interest as components of neuromorphic information processing systems. Previous proposals for spintronic artificial neurons required external stimuli to perform the leaking functionality, one of the three fundamental functions of a leaky integrate-and-fire (LIF) neuron. The use of this ext...Show More

Shape-Based Magnetic Domain Wall Drift for an Artificial Spintronic Leaky Integrate-and-Fire Neuron

Wesley H. Brigner;Naimul Hassan;Lucian Jiang-Wei;Xuan Hu;Diptish Saha;Christopher H. Bennett;Matthew J. Marinella;Jean Anne C. Incorvia;Felipe Garcia-Sanchez;Joseph S. Friedman

Year: 2019 | Volume: 66, Issue: 11 | Journal Article |

SPICE-Only Model for Spin-Transfer Torque Domain Wall MTJ Logic

Xuan Hu;Andrew Timm;Wesley H. Brigner;Jean Anne C. Incorvia;Joseph S. Friedman

IEEE Transactions on Electron Devices
Year: 2019 | Volume: 66, Issue: 6 | Journal Article |
Cited by: Papers (23)
The spin-transfer torque domain wall (DW) magnetic tunnel junction (MTJ) enables spintronic logic circuits that can be directly cascaded without deleterious signal conversion circuitry and is one of the only spintronic devices for which cascading has been demonstrated experimentally. However, experimental progress has been impeded by a cumbersome modeling technique that requires a combination of m...Show More

SPICE-Only Model for Spin-Transfer Torque Domain Wall MTJ Logic

Xuan Hu;Andrew Timm;Wesley H. Brigner;Jean Anne C. Incorvia;Joseph S. Friedman

Year: 2019 | Volume: 66, Issue: 6 | Journal Article |

Graded-Anisotropy-Induced Magnetic Domain Wall Drift for an Artificial Spintronic Leaky Integrate-and-Fire Neuron

Wesley H. Brigner;Xuan Hu;Naimul Hassan;Christopher H. Bennett;Jean Anne C. Incorvia;Felipe Garcia-Sanchez;Joseph S. Friedman

IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Year: 2019 | Volume: 5, Issue: 1 | Journal Article |
Cited by: Papers (29)
Spintronic three-terminal magnetic-tunnel-junction (3T-MTJ) devices have gained considerable interest in the field of neuromorphic computing. Previously, these devices required external circuitry to implement the leaking functionality that leaky integrate-and-fire (LIF) neurons should display. However, the use of external circuitry results in decreased device efficiency. We previously demonstrated...Show More

Graded-Anisotropy-Induced Magnetic Domain Wall Drift for an Artificial Spintronic Leaky Integrate-and-Fire Neuron

Wesley H. Brigner;Xuan Hu;Naimul Hassan;Christopher H. Bennett;Jean Anne C. Incorvia;Felipe Garcia-Sanchez;Joseph S. Friedman

Year: 2019 | Volume: 5, Issue: 1 | Journal Article |

Scaling the CBRAM Switching Layer Diameter to 30 nm Improves Cycling Endurance

Shosuke Fujii;Jean Anne C. Incorvia;Fang Yuan;Shengjun Qin;Fei Hui;Yuanyuan Shi;Yang Chai;Mario Lanza;H.-S. Philip Wong

IEEE Electron Device Letters
Year: 2018 | Volume: 39, Issue: 1 | Journal Article |
Cited by: Papers (24)
Control of cation injection into the switching layer of conductive-bridge random access memory (CBRAM) during switching is a critical factor for CBRAM reliability. Although extrinsic approaches such as the insertion of a transistor in series have proven effective, solutions intrinsic to the CBRAM itself, which are desired for high density cross-point or 3-D vertical memory arrays, are quite limite...Show More

Scaling the CBRAM Switching Layer Diameter to 30 nm Improves Cycling Endurance

Shosuke Fujii;Jean Anne C. Incorvia;Fang Yuan;Shengjun Qin;Fei Hui;Yuanyuan Shi;Yang Chai;Mario Lanza;H.-S. Philip Wong

Year: 2018 | Volume: 39, Issue: 1 | Journal Article |

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