HP Labs Research Articles

High Frequency Response of Non-Volatile Memristors

This paper presents an analytical investigation of the transient and steady-state response of non-volatile memristors to high frequency periodic inputs, using as a case study a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textrm {TaO}_{\textrm {x}}$ </tex-math></inline-formula> -based nano-scale memristor model derived at HP Labs. For the first time, we provide a mathematical proof for the fading memory phenomenon in memristors stimulated by periodic inputs in the high frequency limit. Specifically, we demonstrate that the steady-state response of a non-volatile memristor, exhibiting asymmetric switching kinetics with respect to the polarity of the input, depends only on the amplitude of the testing signal and not on the device initial conditions. Based on the results of our analyses, we provide an alternative method for tuning the memristor state by using high-frequency AC inputs, and introduce a new system-theoretic visualization tool, namely the input-referred High-Frequency Dynamic Route Map (HF-DRM), that allows the reproduction of the memristor time-response to any high-frequency periodic input from each admissible initial condition. The purely theoretical results introduced in this paper could inspire new approaches for modulating the memory states of practical non-volatile memristors.

Effect of Build Parameters on the Mechanical Behavior of Polymeric Materials Produced by Multijet Fusion

The figure depicts the multi-jet fusion printing of specimens used to study the effect of changing build parameters on mechanical properties. At each layer, heat is applied to a surface with fusing agent (black) deposited atop fresh powder. A transparent build volume shows the temperature distribution (white-red color map) on the printed layer. Further details can be found in the article number 2100974 by Grace X. Gu and co-workers. For the image, we would like to credit: "Fabiola Leyva from HP Labs".

Investigation of STDP mechanisms for memristor circuits

Memristive systems have become popular as a research field after the experimental realization of a memristor by the HP Labs research team. Researchers have demonstrated the synaptic behavior of the memristor in addition to its nonlinear and nonvolatile characteristics. Synapses play an important role in the learning process and spike-timing-dependent plasticity (STDP) is a fundamental synaptic learning rule. Because of problems with finding the memristor on the market as a discrete circuit element, researchers have designed many types of memristor circuits to emulate memristors. However, no information is available about the STDP behavior of these designed memristor emulators. This paper investigated the STDP property of some basic memristor circuits and the mathematical and simulation results demonstrated that none of them could support the STDP learning rule.

Elucidating the electrical performance and thermal stability in14-nm FinFETs CMOS technology

Nanoscale device self-heating effects have become an important issue in simulation and fabrication of Bulk and SOI FinFET transistors. In this work, we have investigated a mathematical methodology to capture electrical performance and thermal stability in a 14-nm Bulk and SOI FinFET components. Finite Element method have been used to bring forth our numerical results. We have compared the electrical part of our mathematical model with experimental data obtained by INTEL and HP laboratories and with TCAD simulators. It is revealed that for both SOI and Bulk FinFETs, the I–V characteristics obtained by our model is very close to that obtained by INTEL and HP experimental works. Our results also show a good agreement between the thermal parts of the proposed model with TCAD simulator. On the other hand, we have demonstrated that after 100 ​ns, the 14 ​nm Bulk FinFET supports better the temperature distribution than the 14 ​nm SOI FinFET.

Effects of build positions on the thermal history, crystallization, and mechanical properties of polyamide 12 parts printed by Multi Jet Fusion

ABSTRACT Multi Jet Fusion (MJF) has gradually been utilised for the commercial fabrication of final products. In particular, the mechanical properties of MJF-printed polyamide 12 (MJF PA12) parts have been widely investigated by manipulating the process parameters such as the printing orientation and the cooling rate. In this work, the effects of build positions on the thermal history, crystallization, and mechanical properties of MJF PA12 parts were investigated. The thermal history of printed parts was obtained using the MJF Thermal Prediction Engine software developed by Hewlett-Packard HP Labs (HP Inc.). As compared to the parts printed at the boundaries, those in the centre region experienced a slower cooling rate, which was favourable for crystal growth. Both the crystallite size and crystallinity of the parts in the centre region were larger than those of the parts at the boundaries. The parts printed in the centre region had slightly higher tensile modulus and significantly smaller elongation at break and strain energy density than those printed at the boundaries. The tensile strength of the parts at different locations was comparable. This work serves as a guide to selecting the build position of MJF-printed parts to achieve desirable mechanical properties.

Editorial Special Issue for 50th Birthday of Memristor Theory and Application of Neuromorphic Computing Based on Memristor—Part I

In 1971, Dr. Leon Chua, known as the father of nonlinear circuits and cellular neural networks, postulated the existence of memristor, a portmanteau of memory resistor, in his seminal paper: Memristor-the missing circuit element published in IEEE Transactions on Circuit Theory, the predecessor of IEEE Transactions on Circuits and Systems. Thirty-seven years after he predicted its existence, in the May 1 (2008) issue of the journal <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Nature</i> , a team at HP Labs led by the scientist R. S. Williams proved that the memristor was real by formulating a physics-based model of a memristor and build nanoscale devices in their lab that demonstrate all of the necessary operating characteristics. Since then, the extensive interest of academic and industrial circles on neuromorphic computing based on memristor has been skyrocketed. Moreover, the unusual electrical properties of circuits and systems based on memristor can mimic the functionalities of the human brain, and can provide an in-depth understanding of key design implications of memristor-based memories, such as learning and anticipating. As a result, neuromorphic computing based on memristor is expected to bring significant breakthrough in dynamic neuromorphic memories, memristor-based resistive RAM, non-volatile memory technology, and so on.

Evaluation and Realization of Memristor emulator spice

The fourth missing element of electrical device now are attractive in many applications, memristor is one of the elements which the resistance depends on the historical memory of the current flowing. In this paper, a memristor device were realized, implemented and evaluated using PSPICE package. Where the HP laboratories are manufactured and tests the performance. The relationship between voltage and current characteristics has been measured in different frequencies and voltage to the memristor. The results obtained are compatible with the designed micromodel with the expected performance.

Intelligent Character Recognition System Using Convolutional Neural Network

Computational Linguistics involves the techniques of Computer Science which play a vital role in recognizing written or printed characters such as numbers or letters to change them into a form that the computer can use it efficiently. Convolutional Neural Network differs from other approaches by extracting the features automatically. The proposed approach is capable of recognizing characters in a variety of challenging conditions using the Convolutional Neural Network, where traditional character recognition systems fail, notably in the presence of low resolution, substantial blur, low contrast, and other distortions. Intellectual Character Recognition System is an application that uses Convolutional Neural Network (CNN) to recognize the Tamil character dataset accurately developed by HP Labs India. The novelty of this system is that, it recognizes the characters of the Predominant Tamil language. With the help of suitable datasets consisting of the Tamil Scripts, the model is trained efficiently. This work has produced a training accuracy of 99.16% which is far better compared to the traditional approaches.

From Memristive Materials to Neural Networks.

The information technologies have been increasing exponentially following Moore's law over the past decades. This has fundamentally changed the ways of work and life. However, further improving data process efficiency is facing great challenges because of physical and architectural limitations. More powerful computational methodologies are crucial to fulfill the technology gap in the post-Moore's law period. The memristor exhibits promising prospects in information storage, high-performance computing, and artificial intelligence. Since the memristor was theoretically predicted by L. O. Chua in 1971 and experimentally confirmed by HP Laboratories in 2008, it has attracted great attention from worldwide researchers. The intrinsic properties of memristors, such as simple structure, low power consumption, compatibility with the complementary metal oxide-semiconductor (CMOS) process, and dual functionalities of the data storage and computation, demonstrate great prospects in many applications. In this review, we cover the memristor-relevant computing technologies, from basic materials to in-memory computing and future prospects. First, the materials and mechanisms in the memristor are discussed. Then, we present the development of the memristor in the domains of the synapse simulating, in-memory logic computing, deep neural networks (DNNs) and spiking neural networks (SNNs). Finally, the existent technology challenges and outlook of the state-of-art applications are proposed.

A Qualitative Study of Materials and Fabrication Methodologies for Two Terminal Memristive Systems

Though the concept of the thin film device, known by the name, memristors, as coined by Leon Chua, was theorized in 1971, it was only from the year 2008 that these thin film devices gained broader attention when R. Stanley Williams and his team demonstrated the first practical realization of the memristor at the HP labs . Memristors have been considered as the fourth fundamental circuit element, adding to resistors, capacitors and inductors. Among several other applications, the most unique application of memristors is that, while they occupy very little chip area, the synapses in the brain can be closely modeled using these devices. The memristive device from HP had two platinum electrodes on either side of a titanium dioxide (TiO2) and oxygen deficient titanium dioxide (TiO2-x) layers. The boundary separating the stoichiometric and oxygen deficient TiO2 regions can be shifted on either side under the effect of an applied electric field. The current-voltage characteristics of this device from HP Labs exhibited a pinched hysteresis loop with a zero-crossing, depicting high resistance and low resistance states distinctly. Since the discovery at HP labs, researchers have reported several material choices for the electrodes and the active region (TiO2 – TiO2-x). This paper presents a qualitative study on the material aspects of these nanoscale devices. Also, an exhaustive study of the different methodologies for fabricating such memristive systems is presented in this paper. These two terminal devices can be fabricated in a variety of ways but each method of fabrication has its own set of limitations since these devices operate at the nanoscale. The suitability of the single step and two step nano-imprint lithography (NIL) for memristors has been qualitatively examined in this paper

Science and Technological Understanding of Nano-ionic Resistive Memories (RRAM)

Ion transport in the solid state has been regarded as imperative with regards to high energy density electrochemical storage devices (for instance, batteries) for efficient electric mobility. Of late, there is another niche application involving ion transport in solid state which manifested itself as nonvolatile memory namely memristor. Such memories are classified under the emerging category of novel solid state Resistive Random Access Memories (RRAM). In 2008, HP labs unveiled the first practical memristor device employing TiO2 and non-stoichiometric titania as bilayer stack structure and on both sides of two titania layers platinum (pt) are used as blocking electrode for ions. It is understood that switching fundamentals are correlated to the filamentary conduction in metal oxide memristors owing to the formation and rupture of the filament-like nano-dendrites, one of the key mechanisms widely accepted in the arena of memristor analysis. This paper critically reviews the fundamental materials being employed in novel memristor memories. It is believed that solid electrolytes (fast ion conductors) are the fundamental building blocks of these memories. We have chosen a few archetypes, solid electrolytes are considered and their impact on the state-of-art research in this domain is discussed in detail. An indepth analysis of the fundamentals of resistive switching mechanism involved in various classes of memristive devices viz., Electrochemical Metallization Memories (ECM) and Valence Change Memories (VCM) is elucidated. A few important applications of memristors such as neuristor and artificial synapse in neuromorphic computing are reviewed as well.

Classification of Handwritten Tamil Characters using Variable Length Puzzle Pieces

Offline handwritten character recognition system has been a challenge for Indian scripts, especially for South Indian languages. Huge number of characters of local languages including alphabets, consonants and composite characters make the recognition system more complicated. A good recognition system for subset of Tamil script, a famous South Indian script, is proposed in this work. Variable length feature vector is extracted from the thinned character image. This extracted feature is given to a novel simple classification algorithm which works based on probability. A subset of Tamil script, 20 character classes, is considered for experiment. The samples were taken from HP Labs dataset for Tamil language and a recognition accuracy of 88.15% has been produced.

Benchmarking on offline Handwritten Tamil Character Recognition using convolutional neural networks

Convolutional Neural Networks (CNN) are playing a vital role nowadays in every aspect of computer vision applications. In this paper we have used the state of the art CNN in recognizing handwritten Tamil characters in offline mode. CNNs differ from traditional approach of Handwritten Tamil Character Recognition (HTCR) in extracting the features automatically. We have used an isolated handwritten Tamil character dataset developed by HP Labs India. We have developed a CNN model from scratch by training the model with the Tamil characters in offline mode and have achieved good recognition results on both the training and testing datasets. This work is an attempt to set a benchmark for offline HTCR using deep learning techniques. This work have produced a training accuracy of 95.16% which is far better compared to the traditional approaches.

Numerical simulations of the linear drift memristor model

Memristor is a passive element theoretically proposed by Leon Chua in the 1970’s. It started to receive attention after 2008, when researchers from the HP Labs presented a device with memristive properties. Since then, several models have been proposed to describe the memristor. In this work, we analyze the linear drift model, comparing the numerical solutions with analytical solutions and SPICE simulations. We demonstrate that different solutions can be found depending on the method and parameter set.

An interview with Indrajit Roy

Indrajit Roy is a staff engineer at Google. He is currently working on peta-scale distributed databases. Previously, he was a principal researcher at HP Labs where he led the development of Distributed R, an open source HP product that brings the benefits of parallelism to data scientists. Roy received his Ph.D. in computer science from UT Austin. He is also an inaugural member of the ACM Future of Computing Academy.

A novel nearest interest point classifier for offline Tamil handwritten character recognition

Handwritten character recognition is the most widely used branch of study in image pattern recognition. Tamil, the official language of Tamil Nadu in South India, Sri Lanka, Singapore and Malaysia, has a script which contains many loops and compound characters, with small differences between character classes. Most of the research on offline Tamil handwritten character recognition system was done only on few character classes as it is very difficult to distinguish between minute dissimilarities of large character classes. It is important to design a complete recognition system that can process all character classes of Tamil and distinguish natural variability between inter-class images. Unlike conventional machine learning approaches for pattern recognition problems, we have proposed a nearest interest point classifier, which can choose sufficient and necessary subset of features from a variable length high dimensional feature vector. Since this is a practical problem, in this work, a study on image to image matching is included through feature analysis without using machine learning approaches. The proposed algorithm gave a good recognition accuracy for all the character classes on the standard database available for Tamil, HP Labs offline Tamil handwritten character database. Our proposed classifier produced a recognition accuracy of 90.2% while including the whole dataset. The method has been compared with the standard classifiers and has been proved to be a state-of-the-art performance in recognition of accuracy over the previous results given in the literature.

Knowledge Protection in Firms: A Conceptual Framework and Evidence from HP Labs

This paper proposes a simple framework to examine organizational methods of knowledge protection. The framework highlights a basic trade‐off between improving decision‐making and innovation through communication and mitigating security risks by imposing restrictions on communication flows. The trade‐off is mediated by factors such as the sensitivity of information, the degree to which employees can be trusted to handle sensitive information appropriately, and firms’ investments in legal protection mechanisms. Evidence from HP Labs supports the basic predictions of the model, in particular the importance of employee trustworthiness and internalized codes of behavior in promoting open communication. Our interviews also suggest a potential conflict between two of the most important appropriability mechanisms: secrecy and lead‐time advantage.

The influence of Hipparchus in Antikythera mechanism

Around 1900, a group of sponge divers, pulled out from an ancient Roman shipwreck, near the Antikythera island, a piece of a hardly distinguishable geared mechanism which is known since then as “The Antikythera mechanism”. Until now the exact use of some of its survived gears is investigated and there are speculations about its missing parts. In 2005 two revolutionary technical methods were applied on Antikythera mechanism. The first is known as : (3D) X-ray computed tomography (CT) by X-Tek Systems Ltd, with the use of which many hidden details under the corrosion, came to light. The second is known as : Polynomial Texture Mapping (PTM) which was applied by HP Labs, revealing engraved symbols and letters on Antikythera mechanism surfaces. After all these years of research, a more comprehensive picture of this machinery was built, leading to the conclusion that it was used as a multi functional “astronomical calculator” according to Freeth et al. (2006b) with many impressive capabilities such as:•a prediction of upcoming Solar and Lunar eclipses•a calculator of the four astronomical cycles (Metonic cycle, Callippic cycle, Saros cycle, Exeligmos cycle)•moon phases indication•Sun and Moon position in zodiacThe whole mechanical construction seemed covered by a wooden case with a front and a back door with engraved inscriptions on them. According to Freeth et al. (2006b) “The inscriptions support suggestions of mechanical display of planetary positions” proving the existence of a missing planetary subsystem. In this decrypted text, according to Freeth et al. (2006b), astronomical terms have been recognized like stations ▪ like statio,conjunctions and according to Jones (2017), there are references about the names of the five known planets. Researchers are trying for years to present planetary activity on their models suggesting different solutions with a common characteristic. In all models of planetary motion, the epicyclic theory is applied. Although this theory is precise enough for planets with negligible eccentricity, fails to demonstrate the real recorded apparent orbit of planets with significant eccentricity, like Mercury’s which is 0.205. On the other hand in Antikythera mechanism remains, there is an advanced device, known as the “pin & slot”, which simulates the non-uniform motion of the moon. According to Moussas (2009)...The difference between Kepler’s predictions on the angular velocity of the Moon during the month and the mechanism’s prediction is of the order 1/400 .... So if the constructor used a specialized gearing to model the moon’s orbit eccentricity which is 0.0549 why not do the same for Mercury’s case with an eccentricity of 3.7 times bigger? Many speculations about the Antikythera mechanism’s purpose have already been expressed, but nevertheless, accuracy is its undeniable feature which should be reflected in the mechanism’s astronomical indications. So, there’s a question that might be answered. ”Is it possible, the planets eccentricity can be simulated using the pin & slot device?”. This research tries to find an answer and proposes a feasible epicyclic gearing which can simulate the geocentric apparent path of Mercury. The construction is taking seriously the planet’s eccentricity, trying to minimize the discrepancy between expected and indicated measurements. Our proposal is based on theories known at the time Antikythera mechanism was constructed.

Memristors and More

Welcome to IEEE Micros special issue on memristor-based computing. Memristors were a theoretical concept (proposed by circuit theorist Leon Chua in 1971) for over three decades until HP Labs built a physical device in 2008. Memristors have gained substantial interest since then because of their nonvolatility; a memristor remembers its past, even when the supply voltage is turned off. Moreover, memristors facilitate co-mingling of storage and computation. A number of memristor applications have been proposed and evaluated, including resistive memory, memristor-based logic, and even neuromorphic computing.

(Invited) Beyond CMOS: Memristor and Its Application for Next Generation Storage and Computing

To break the Moore’s Law, beyond CMOS that is post-silicon technologies has gained great attention in recent years, especially, facing the immensely expensive computation in terms of speed and energy in the current big data environment. Novel devices and circuits and computing architectures based on them have appeared to improve the computing efficiency, such as spin device, phase change device, and memristor. Among them, memristor is considered as one of the most promising candidate for the next generation storage and computing own to its high speed, scalability, low energy, and good comparability with CMOS technology. Being found as the fourth missing circuit element by researchers from HP labs in 2008, memristor is a two-terminal device in a MIM (metal-insulator-metal) structure that store information by resistance states. Multiple resistance states can be obtained by controlling the outside bias amplitude and duration. It is organized in cross-point structure with ultra-high density – the memristor sits between the horizontal and vertical metal lines. Research of employing the memristor-based cross-point array in nonvolatile memory, which is called resistive random access memory (ReRAM) has gained great success as an alternative technology of flash memory. Besides that, memristor is also got enormous attention in novel computing architecture, such as in-memory computing and the brain-inspired computing. The brain-inspired computing, also well known as neuromorphic computing, refers to implementing the computation in neural systems by utilizing a specific VLSI hardware system. In traditional CMOS domain, many systems in digital, analog and mixed-signal formats have been presented and demonstrated, aiming at improving computing and data communication efficiency, i.e., high computation speed with low cost. The memristor device appears as a rebirth of this technology because of its above unique features and capability to perform like synapsed naturally. In this paper, we will summarize our work in memory and neuromorphic computing system designs by leveraging the memristor device. It includes three major parts: 1) Memristor devices modeling and related circuits design in resistive memory (ReRAM) technology by investigating their physical mechanism, statistical analysis, and intrinsic challenges. A weighted sensing scheme which assigns different weights to the cells on different bit lines was proposed. The area and power overhead of peripheral circuitry was effectively reduced while minimizing the amplitude of sneak paths. 2) Neuromorphic computing system designs by leveraging memristor devices and algorithm scaling in neural network and machine learning algorithms based on the similarity between memristive effect and biological synaptic behavior. First, a spiking neural network (SNN) with a rate coding model was developed in algorithm level and then mapped to hardware design for supervised learning. In addition, to further speed and accuracy improvement, another neuromorphic system adopting analog input signals with different voltage amplitude and a current sensing scheme was built. Moreover, the use of a single memristor crossbar for each neural network layer was explored. 3) The application-specific optimization for further reliability improvement of the developed neuromorphic systems. In this thesis, the impact of device failure on the memristor-based neuromorphic computing systems for cognitive applications was evaluated. Then, a retraining and a remapping design in algorithm level and hardware level were developed to rescue the large accuracy loss. Our results indicate that the ReRAM design with the weighted sensing scheme save more than 50% area and power consumption. The spiking-based design achieves more than 50% energy savings, while the average probability of failed recognition increase only 1.46% and 5.99% in the feedforward and Hopfield implementations, respectively. Our evaluation based on real device testing data shows that our rescuing design can recover 99.3% of accuracy for MNIST digit recognition when 20% random defects are considered. Our fabricated neuro-chips with CMOS technology in GlobalFoundries 130nm has more than 90% accuracy for MNIST dataset classification.