Article
Nanoscience & Nanotechnology
Timea Nora Torok, Janos Gergo Fehervari, Gabor Meszaros, Laszlo Posa, Andras Halbritter
Summary: Resistive switching memory devices have the potential to achieve artificial neural networks and nonconventional computing. Studying single resistive switching elements is crucial for utilizing their characteristics for computation. The variability of set time, the timespan before the transition from a high-resistance OFF state to a low-resistance ON state, is key to utilizing the inherent stochasticity of resistance switching. In this study, the set time statistics in nanometer-sized graphene-SiOx-graphene resistive switching memory devices were investigated. The study demonstrated a universal variance of logarithmic set time values, which is characteristic of a nucleation-driven crystallization process. The correlation between OFF state resistance and set time was observed and the tunability of set time statistics was explored by changing the reset amplitude parameter in sequential pulsed measurements. This phenomenon could be useful for controlling stochasticity in memristor-based probabilistic computing applications.
ACS APPLIED NANO MATERIALS
(2022)
Article
Physics, Applied
Ziqi Chen, Hao Tong, Xin Li, Lun Wang, Ruizhe Zhao, Wei Gu, Xiangshui Miao
Summary: This paper presents a multiple layer device for investigating the impact of electric field on the conductance switching of GeTe phase change material, revealing that the ovonic threshold switching cannot be induced solely by electric field in amorphous chalcogenide film. A modified thermal-assist model based on the Poole-Frenkel mechanism is proposed to further study the OTS mechanism and application of PCM.
APPLIED PHYSICS LETTERS
(2021)
Review
Materials Science, Multidisciplinary
Nishant Saxena, Anbarasu Manivannan
Summary: The article reviews the systematic understanding of threshold switching properties in various chalcogenide materials, Ovonic threshold switching and Ovonic memory switching, and discusses the role of threshold switching in governing programming speed based on research efforts over the last six decades. It also explores the realization of threshold switching in picosecond timescale and proposes a scheme of material classification for phase-change memory programming.
PHYSICA STATUS SOLIDI-RAPID RESEARCH LETTERS
(2022)
Article
Chemistry, Multidisciplinary
Meng Xu, Qundao Xu, Rongchuan Gu, Songyou Wang, Cai-Zhuang Wang, Kai-Ming Ho, Zhongrui Wang, Ming Xu, Xiangshui Miao
Summary: It is discovered that a large fraction of over-coordinated clusters fails to generate mid-gap states, which are probably caused by hypervalent bonding, a multi-centered covalent bond participated by delocalized lone-pair electrons. In practical applications, compatible dopants can be used to change the number of hypervalent bonds, thus controlling the number of mid-gap states and consequently the performance of PCM and OTS materials. These results reveal the origin of mid-gap states in chalcogenide glasses, enabling extensive control in the development of pioneering electrical switching materials.
ADVANCED FUNCTIONAL MATERIALS
(2023)
Article
Materials Science, Multidisciplinary
Lun Wang, Zixuan Liu, Zhuoran Zhang, Jiangxi Chen, Jinyu Wen, Ruizhe Zhao, Hao Tong, Xiangshui Miao
Summary: In this study, a refresh operation is proposed to solve the poor thermal stability issue of OTS selectors based on GeTe. The switching performance of GeTe selectors can be fully restored to the original level by the refresh operation and exhibit good consistency, making them applicable in the BEOL process. Furthermore, the endurance of the device is significantly improved after applying the refresh operation. The effectiveness of this refresh operation is also verified for OTS devices based on the commonly used GeSe system, suggesting its wide applicability to other OTS devices.
JOURNAL OF MATERIALS CHEMISTRY C
(2023)
Article
Materials Science, Multidisciplinary
Rongchuan Gu, Meng Xu, Yongpeng Liu, Yinghua Shen, Chong Qiao, Cai-Zhuang Wang, Kai-Ming Ho, Songyou Wang, Ming Xu, Xiangshui Miao
Summary: Si doping plays a crucial role in OVTS materials by improving thermal stability and reducing threshold voltage. This study reveals the atomic mechanisms of Si doping in OVTS materials through first-principles calculations, providing a theoretical basis for the application of Si doping in OVTS materials.
JOURNAL OF MATERIALS CHEMISTRY C
(2023)
Article
Chemistry, Multidisciplinary
Bin Chen, Xue-Peng Wang, Fangying Jiao, Long Ning, Jiaen Huang, Jiatao Xie, Shengbai Zhang, Xian-Bin Li, Feng Rao
Summary: This study shows that by simplifying the composition and miniaturizing the dimensions of traditional GeSbTe-like phase-change materials (PCMs), resistance drift in PCRAM devices can be effectively suppressed. It is demonstrated that a thin Sb film, with an optimal thickness of only 4 nm, enables precise multilevel programming with ultralow resistance drift coefficients. This advancement is achieved through the slightly changed Peierls distortion in Sb and the less-distorted atomic configurations across the Sb/SiO2 interfaces.
Article
Multidisciplinary Sciences
Shanming Hu, Yuhuang Fang, Chen Liang, Matti Turunen, Olli Ikkala, Hang Zhang
Summary: Inspired by biological systems, this study demonstrates thermally trainable hydrogel systems consisting of two thermoresponsive polymers. The volumetric response of the system can be enhanced or decreased through a training process, achieving different network design effects.
NATURE COMMUNICATIONS
(2023)
Article
Optics
Jiafei Chen, Shu Zong, Xiaoshan Liu, Guiqiang Liu, Xuefeng Zhan, Zhengqi Liu
Summary: In this work, a theoretical chiral metasurface absorber composed of periodically serrated GST resonators is proposed and numerically demonstrated. The chiral response is strongly enhanced by utilizing the gradient geometry, and the CD value can be modified and switched by controlling the gradient difference in the serrated GST resonator.
Article
Chemistry, Physical
Hao Liu, Huang Gong, Kai Liu, Keyuan Ding, Jintao Chen, Zhaoyang Liu, Feng Rao
Summary: Through ab initio simulation, researchers have observed reversible semiconductor-metal transitions in boron tellurides, providing insight into the switching principles of selectors. They found that fine structural tunings and conversions between covalent and hypervalent bonding schemes are responsible for the transient switching in conductance.
CHEMISTRY OF MATERIALS
(2023)
Article
Nanoscience & Nanotechnology
James T. Best, Mohammad Ayaz Masud, Maarten P. Boer, Gianluca Piazza
Summary: The design, modeling, and experimental validation of a highly scalable phase change electromechanical relay are presented. The relay utilizes phase change material to actuate and change its state. Finite element analysis models were used to predict temperature distributions and quench times, and the experimental results confirmed the accuracy of the models.
ADVANCED ELECTRONIC MATERIALS
(2022)
Article
Chemistry, Multidisciplinary
Tae Wan Park, Woon Ik Park
Summary: This study successfully reduced the writing current of phase change memory (PCM) by employing self-assembly of Si-containing block copolymers (BCPs), which can locally block the current path of the contact between high resistive film and phase-change material, leading to significant power reduction. This BCP-based approach has the potential to be extended to other non-volatile memory devices, such as resistive switching memory and magnetic storage devices.
Article
Materials Science, Multidisciplinary
Mozhikunnam Sreekrishnan Arjunan, Nishant Saxena, Anirban Mondal, Tejendra Dixit, Kumaran Nair Valsala Devi Adarsh, Anbarasu Manivannan
Summary: By irradiating nanosecond laser pulses on thin In(3)SbTe(2)films, eight uniform optical states with high stability and low noise were demonstrated, showing potential for reliable phase change photonic memory devices.
PHYSICA STATUS SOLIDI-RAPID RESEARCH LETTERS
(2021)
Article
Multidisciplinary Sciences
Nishant Saxena, Rajamani Raghunathan, Anbarasu Manivannan
Summary: This research demonstrates how to achieve picosecond threshold switching in phase change memory to make the operation speed closer to computing speed. Based on experimental results, faster switching speed and low delay time have been achieved, validating the electronic nature of threshold switching.
SCIENTIFIC REPORTS
(2021)
Article
Engineering, Electrical & Electronic
Lun Wang, Wang Cai, Da He, Qi Lin, Daixing Wan, Hao Tong, Xiangshui Miao
Summary: In this study, a C-doped GeTex Ovonic threshold switching (OTS) selector with a via-hole structure was fabricated, showing comparable performance to commercial GeSe-based selectors. The selector demonstrated low off-current, low threshold voltage, a large voltage window, high selectivity, satisfactory endurance, and fast operating speed. Additionally, the selector exhibited self-limited on-current characteristics, with reasons behind the off-current reduction and consistency improvement by C doping studied through First-principles calculations.
IEEE ELECTRON DEVICE LETTERS
(2021)
Article
Multidisciplinary Sciences
J. Feldmann, N. Youngblood, M. Karpov, H. Gehring, X. Li, M. Stappers, M. Le Gallo, X. Fu, A. Lukashchuk, A. S. Raja, J. Liu, C. D. Wright, A. Sebastian, T. J. Kippenberg, W. H. P. Pernice, H. Bhaskaran
Summary: With the advancement of technology, the demand for fast processing of large amounts of data is increasing, making highly parallelized, fast, and scalable hardware crucial. The integration of photonics can serve as the optical analogue of an application-specific integrated circuit, enabling photonic in-memory computing and efficient computational hardware.
Correction
Multidisciplinary Sciences
J. Feldmann, N. Youngblood, M. Karpov, H. Gehring, X. Li, M. Stappers, M. Le Gallo, X. Fu, A. Lukashchuk, A. S. Raja, J. Liu, C. D. Wright, A. Sebastian, T. J. Kippenberg, W. H. P. Pernice, H. Bhaskaran
Article
Chemistry, Multidisciplinary
Syed Ghazi Sarwat, Timothy M. Philip, Ching-Tzu Chen, Benedikt Kersting, Robert L. Bruce, Cheng-Wei Cheng, Ning Li, Nicole Saulnier, Matthew BrightSky, Abu Sebastian
Summary: Phase-change memory devices are utilized in in-memory computing to compute without needing to transfer data between memory and processing units. The projection of phase configurations onto stable elements within the device is a promising approach to address nonidealities. By investigating the projection mechanism in prominent phase-change memory device architectures, such as the mushroom-type phase-change memory, the key attributes and operational principles of nanoscale projected Ge2Sb2Te5 devices are understood.
ADVANCED FUNCTIONAL MATERIALS
(2021)
Article
Chemistry, Multidisciplinary
Benedikt Kersting, Syed Ghazi Sarwat, Manuel Le Gallo, Kevin Brew, Sebastian Walfort, Nicole Saulnier, Martin Salinga, Abu Sebastian
Summary: Chalcogenide phase change materials are utilized for non-volatile, low-latency storage-class memory and new forms of computing, but face challenges with temporal drift in electrical resistance. Research shows that the efficacy of observation is influenced by the observable timescale, and experimental measurements of drift onset can be conducted using threshold-switching voltage. This additional feature of structural relaxation dynamics serves as a new benchmark for evaluating classical models explaining drift.
ADVANCED FUNCTIONAL MATERIALS
(2021)
Article
Computer Science, Hardware & Architecture
Martino Dazzi, Abu Sebastian, Thomas Parnell, Pier Andrea Francese, Luca Benini, Evangelos Eleftheriou
Summary: In-memory computing is a new computing paradigm that enables deep-learning inference with higher energy-efficiency and lower latency. Communication fabric is a key challenge in this paradigm, and we propose a graph-based communication structure suitable for convolutional neural networks, achieving efficient pipelined execution. Our proposed topology shows lower bandwidth requirements per communication channel compared to established communication topologies, and we demonstrate a hardware implementation mapping ResNet-32 onto an IMC core array interconnected via this communication fabric.
IEEE TRANSACTIONS ON COMPUTERS
(2021)
Article
Engineering, Electrical & Electronic
Riduan Khaddam-Aljameh, Michele Martemucci, Benedikt Kersting, Manuel Le Gallo, Robert L. Bruce, Matthew BrightSky, Abu Sebastian
Summary: By designing unit-cell arrays and implementing diagonal connections, we have successfully addressed challenges such as parallel writing and computational precision in memristive crossbar arrays.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2021)
Review
Multidisciplinary Sciences
Mario Lanza, Abu Sebastian, Wei D. Lu, Manuel Le Gallo, Meng-Fan Chang, Deji Akinwande, Francesco M. Puglisi, Husam N. Alshareef, Ming Liu, Juan B. Roldan
Summary: Memristive devices, which can change their resistance and memory state, have potential applications in various fields. However, there are still challenges to be addressed, including performance and reliability issues.
Article
Computer Science, Hardware & Architecture
Chuteng Zhou, Fernando Garcia Redondo, Julian Buchel, Irem Boybat, Xavier Timoneda Comas, S. R. Nandakumar, Shidhartha Das, Abu Sebastian, Manuel Le Gallo, Paul N. Whatmough
Summary: This article discusses the importance of high energy efficiency in always-on TinyML perception tasks in IoT applications and proposes the use of analog compute-in-memory (CiM) with nonvolatile memory (NVM) to achieve this goal. The authors introduce AnalogNets model architectures and a comprehensive training methodology to maintain accuracy in the presence of analog nonidealities and low-precision data converters. They also present AON-CiM, a programmable phase-change memory (PCM) analog CiM accelerator, designed to reduce the complexity and cost of interconnects. Evaluation results show promising accuracy and efficiency for KWS and VWW tasks.
Article
Multidisciplinary Sciences
Malte J. Rasch, Charles Mackin, Manuel Le Gallo, An Chen, Andrea Fasoli, Frederic Odermatt, Ning Li, S. R. Nandakumar, Pritish Narayanan, Hsinyu Tsai, Geoffrey W. Burr, Abu Sebastian, Vijay Narayanan
Summary: In this study, a hardware-aware retraining approach is developed to examine the accuracy of analog in-memory computing across multiple network topologies and investigate sensitivity and robustness to a broad set of nonidealities. By introducing a realistic crossbar model, significant improvement is achieved compared to earlier retraining approaches. The results show that many larger-scale deep neural networks can be successfully retrained to show iso-accuracy with the floating point implementation, and nonidealities that add noise to the inputs or outputs have the largest impact on accuracy.
NATURE COMMUNICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Manuel Le Gallo, Riduan Khaddam-Aljameh, Milos Stanisavljevic, Athanasios Vasilopoulos, Benedikt Kersting, Martino Dazzi, Geethan Karunaratne, Matthias Brandli, Abhairaj Singh, Silvia M. Mueller, Julian Buchel, Xavier Timoneda, Vinay Joshi, Malte J. Rasch, Urs Egger, Angelo Garofalo, Anastasios Petropoulos, Theodore Antonakopoulos, Kevin Brew, Samuel Choi, Injo Ok, Timothy Philip, Victor Chan, Claire Silvestre, Ishtiaq Ahsan, Nicole Saulnier, Vijay Narayanan, Pier Andrea Francese, Evangelos Eleftheriou, Abu Sebastian
Summary: A multicore analogue in-memory computing chip designed and fabricated in 14 nm complementary metal-oxide-semiconductor technology with backend-integrated phase-change memory is reported. It can be used to reduce the latency and energy consumption of deep neural network inference tasks by performing computations within memory. The chip features interconnection of 64 AIMC cores via an on-chip communication network and implements digital activation functions and additional processing involved in individual convolutional layers and long short-term memory units.
NATURE ELECTRONICS
(2023)
Article
Engineering, Electrical & Electronic
Dennis Christensen, Regina Dittmann, Bernabe Linares-Barranco, Abu Sebastian, Manuel Le Gallo, Andrea Redaelli, Stefan Slesazeck, Thomas Mikolajick, Sabina Spiga, Stephan Menzel, Ilia Valov, Gianluca Milano, Carlo Ricciardi, Shi-Jun Liang, Feng Miao, Mario Lanza, Tyler J. Quill, Scott T. Keene, Alberto Salleo, Julie Grollier, Danijela Markovic, Alice Mizrahi, Peng Yao, J. Joshua Yang, Giacomo Indiveri, John Paul Strachan, Suman Datta, Elisa Vianello, Alexandre Valentian, Johannes Feldmann, Xuan Li, Wolfram H. P. Pernice, Harish Bhaskaran, Steve Furber, Emre Neftci, Franz Scherr, Wolfgang Maass, Srikanth Ramaswamy, Jonathan Tapson, Priyadarshini Panda, Youngeun Kim, Gouhei Tanaka, Simon Thorpe, Chiara Bartolozzi, Thomas A. Cleland, Christoph Posch, Shihchii Liu, Gabriella Panuccio, Mufti Mahmud, Arnab Neelim Mazumder, Morteza Hosseini, Tinoosh Mohsenin, Elisa Donati, Silvia Tolu, Roberto Galeazzi, Martin Ejsing Christensen, Sune Holm, Daniele Ielmini, N. Pryds
Summary: This article introduces the characteristics and advantages of von Neumann architecture and neuromorphic computing systems. While traditional von Neumann architecture is powerful, it has high power consumption and cannot handle complex data. Neuromorphic computing systems, inspired by biological concepts, can achieve lower power consumption for storing and processing large amounts of digital information. The aim of this article is to provide perspectives on the current state and future challenges in the field of neuromorphic technology, and to provide a concise yet comprehensive introduction and future outlook for readers.
NEUROMORPHIC COMPUTING AND ENGINEERING
(2022)
Article
Engineering, Electrical & Electronic
Manuel Le Gallo, S. R. Nandakumar, Lazar Ciric, Irem Boybat, Riduan Khaddam-Aljameh, Charles Mackin, Abu Sebastian
Summary: In-memory computing is an efficient approach that utilizes the physical attributes of memory devices to perform computational tasks. However, the computational accuracy of this approach is currently limited due to inter-device variability, inhomogeneity, and randomness in analog memory devices. Bit slicing, a technique for constructing high precision processors, shows promise in overcoming this limitation. This study assesses the computational error in in-memory matrix-vector multiplications using bit slicing, and emphasizes the need to minimize analog matrix representation error through averaging within a given dynamic range.
NEUROMORPHIC COMPUTING AND ENGINEERING
(2022)
Proceedings Paper
Engineering, Electrical & Electronic
Michele Martemucci, Benedikt Kersting, Riduan Khaddam-Aljameh, Irem Boybat, S. R. Nandakumar, Urs Egger, Matthew Brightsky, Robert L. Bruce, Manuel Le Gallo, Abu Sebastian
Summary: The proposed weight mapping algorithm efficiently programs a synaptic unit composed of multiple phase change memory devices, showing resilience to device-level non-idealities and yield. The algorithm is experimentally validated on a prototype PCM unit cell fabricated in the 90nm CMOS technology node.
2021 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS)
(2021)
Article
Mathematical & Computational Biology
Martino Dazzi, Abu Sebastian, Luca Benini, Evangelos Eleftheriou
Summary: In-memory computing (IMC) is a non-von Neumann paradigm that offers energy-efficient, high throughput hardware for deep learning applications. This approach requires a rethink of architectural design choices due to its different execution pattern compared to previous computational paradigms. When applied to Convolution Neural Networks (CNNs), IMC hardware can achieve throughput and latency beyond current state-of-the-art for image classification tasks.
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
(2021)
Proceedings Paper
Engineering, Multidisciplinary
R. L. Bruce, S. Ghazi Sarwat, I Boybat, C-W Cheng, W. Kim, S. R. Nandakumar, C. Mackin, T. Philip, Z. Liu, K. Brew, N. Gong, I Ok, P. Adusumilli, K. Spoon, S. Ambrogio, B. Kersting, T. Bohnstingl, M. Le Gallo, A. Simon, N. Li, I Saraf, J-P Han, L. Gignac, J. M. Papalia, T. Yamashita, N. Saulnier, G. W. Burr, H. Tsai, A. Sebastian, V Narayanan, M. BrightSky
Summary: Phase change memory (PCM) is being considered for non-von Neumann accelerators for deep neural networks based on in-memory computing. Conductance drift and noise are key challenges for reliable storage of synaptic weights in such accelerators. The integration of a projection liner into multilevel mushroom-type PCM devices demonstrates mitigation of conductance drift and noise, with further improvement shown by combining with a low-drift phase-change material. Large-scale experiments confirm lower drift and device-to-device drift variability for devices with projection liner, crucial for in-memory computing accelerators.
2021 IEEE INTERNATIONAL RELIABILITY PHYSICS SYMPOSIUM (IRPS)
(2021)
