Semiconductor Memory Applications in Radiation Environment, Hardware Security and Machine Learning System

abstract: Semiconductor memory is a key component of the computing systems. Beyond the conventional memory and data storage applications, in this dissertation, both mainstream and eNVM memory technologies are explored for radiation environment, hardware security system and machine learning applications. In the radiation environment, e.g. aerospace, the memory devices face different energetic particles. The strike of these energetic particles can generate electron-hole pairs (directly or indirectly) as they pass through the semiconductor device, resulting in photo-induced current, and may change the memory state. First, the trend of radiation effects of the mainstream memory technologies with technology node scaling is reviewed. Then, single event effects of the oxide based resistive switching random memory (RRAM), one of eNVM technologies, is investigated from the circuit-level to the system level. Physical Unclonable Function (PUF) has been widely investigated as a promising hardware security primitive, which employs the inherent randomness in a physical system (e.g. the intrinsic semiconductor manufacturing variability). In the dissertation, two RRAM-based PUF implementations are proposed for cryptographic key generation (weak PUF) and device authentication (strong PUF), respectively. The performance of the RRAM PUFs are evaluated with experiment and simulation. The impact of non-ideal circuit effects on the performance of the PUFs is also investigated and optimization strategies are proposed to solve the non-ideal effects. Besides, the security resistance against modeling and machine learning attacks is analyzed as well. Deep neural networks (DNNs) have shown remarkable improvements in various intelligent applications such as image classification, speech classification and object localization and detection. Increasing efforts have been devoted to develop hardware accelerators. In this dissertation, two types of compute-in-memory (CIM) based hardware accelerator designs with SRAM and eNVM technologies are proposed for two binary neural networks, i.e. hybrid BNN (HBNN) and XNOR-BNN, respectively, which are explored for the hardware resource-limited platforms, e.g. edge devices.. These designs feature with high the throughput, scalability, low latency and high energy efficiency. Finally, we have successfully taped-out and validated the proposed designs with SRAM technology in TSMC 65 nm. Overall, this dissertation paves the paths for memory technologies’ new applications towards the secure and energy-efficient artificial intelligence system.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Advances in Solid State Circuit Technologies

This book brings together contributions from experts in the fields to describe the current status of important topics in solid-state circuit technologies. It consists of 20 chapters which are grouped under the following categories: general information, circuits and devices, materials, and characterization techniques. These chapters have been written by renowned experts in the respective fields making this book valuable to the integrated circuits and materials science communities. It is intended for a diverse readership including electrical engineers and material scientists in the industry and academic institutions. Readers will be able to familiarize themselves with the latest technologies in the various fields

Nanoscale Carbon-Based Memory Devices

Amorphous carbon-based memories have gained traction in recent years due to their good scalability and switching performance and are an important contender to close the performance gap between fast but volatile DRAM and slow but non-volatile flash memory. A writing and erasing process driven by the electrically induced formation and rupture of a conductive filament permits switching times in the range of a few nanoseconds. Further, the memristive property of amorphous carbon allows the implementation of beyond von Neumann computation paradigms. However, ‘pure’ amorphous memories have a low cyclic endurance. To overcome this and to exploit beyond von Neumann computation, devices based on oxygenated amorphous carbon were employed here. The first part of this thesis evaluated the switching performance and data retention capabilities of tetrahedral amorphous carbon memories. Switching times below 10 ns were achieved for the SET as well as for the RESET times. An energy consumption below 1 pJ was obtained, while data could be retained for more than 300 s at 450 °C. Further, evidence was provided that the SET process is not induced by an electric field alone. A finite-element simulation was employed in the second part of this thesis to reproduce the experimentally determined conductivity of tetrahedral amorphous carbon (ta-C) memory devices and to shine light on the conditions at the onset switching from the high to low resistance states (dielectric breakdown). The maximum temperature observed at dielectric breakdown was 1615 K. It was found that a reduction of the lateral cell radius from 25 nm to 15 nm and 10 nm increases the switching performance by reducing the switching current from 34 µA to 20 µA and 8 µA. The third part of this thesis evaluated the switching performance, temperature stability, multilevel storage and memcomputing capabilities of oxygenated amorphous carbon. Switching times below 10 ns for both, SET and RESET were demonstrated. A 3-level (1 1 /2 bits) data storage was achieved using three different resistance states. Further, a memcomputing approach was implemented using a base-16 accumulation response with energy consumptions as low as <100 fJ per pulse. Additionally, a finite element simulation of a device in the low resistance state (LRS) was used to illustrate the correlation between device resistance and Joule heating effects

Addressing the RRAM Reliability and Radiation Soft-Errors in the Memory Systems

With the continuous and aggressive technology scaling, the design of memory systems becomes very challenging. The desire to have high-capacity, reliable, and energy efficient memory arrays is rising rapidly. However, from the technology side, the increasing leakage power and the restrictions resulting from the manufacturing limitations complicate the design of memory systems. In addition to this, with the new machine learning applications, which require tremendous amount of mathematical operations to be completed in a timely manner, the interest in neuromorphic systems has increased in recent years. Emerging Non- Volatile Memory (NVM) devices have been suggested to be incorporated in the design of memory arrays due to their small size and their ability to reduce leakage power since they can retain their data even in the absence of power supply. Compared to other novel NVM devices, the Resistive Random Access Memory (RRAM) device has many advantages including its low-programming requirements, the large ratio between its high and low resistive states, and its compatibility with the Complementary Metal Oxide Semiconductor (CMOS) fabrication process. RRAM device suffers from other disadvantages including the instability in its switching dynamics and its sensitivity to process variations. Yet, one of the popular issues hindering the deployment of RRAM arrays in products are the RRAM reliability and radiation soft-errors. The RRAM reliability soft-errors result from the diffusion of oxygen vacations out of the conductive channels within the oxide material of the device. On the other hand, the radiation soft-errors are caused by the highly energetic cosmic rays incident on the junction of the MOS device used as a selector for the RRAM cell. Both of those soft-errors cause the unintentional change of the resistive state of the RRAM device. While there is research work in literature to address some of the RRAM disadvantages such as the switching dynamic instability, there is no dedicated work discussing the impact of RRAM soft-errors on the various designs to which the RRAM device is integrated and how the soft-errors can be automatically detected and fixed. In this thesis, we bring the attention to the need of considering the RRAM soft-errors to avoid the degradation in design performance. In addition to this, using previously reported SPICE models, which were experimentally verified, and widely adapted system level simulators and test benches, various solutions are provided to automatically detect and fix the degradation in design performance due to the RRAM soft-errors. The main focus in this work is to propose methodologies which solve or improve the robustness of memory systems to the RRAM soft-errors. These memories are expected to be incorporated in the current and futuristic platforms running the advanced machine learning applications. In more details, the main contributions of this thesis can be summarized as: - Provide in depth analysis of the impact of RRAM soft-errors on the performance of RRAM-based designs. - Provide a new SRAM cell which uses the RRAM device to reduce the SRAM leakage power with minimal impact on its read and write operations. This new SRAM cell can be incorporated in the Graphical Processing Unit (GPU) design used currently in the implementation of the machine learning platforms. - Provide a circuit and system solutions to resolve the reliability and radiation soft-errors in the RRAM arrays. These solution can automatically detect and fix the soft-errors with minimum impact on the delay and energy consumption of the memory array. - A framework is developed to estimate the effect of RRAM soft-errors on the performance of RRAM-based neuromorphic systems. This actually provides, for the first time, a very generic methodology through which the device level RRAM soft-errors are mapped to the overall performance of the neuromorphic systems. Our analysis show that the accuracy of the RRAM-based neuromorphic system can degrade by more than 48% due to RRAM soft-errors. - Two algorithms are provided to automatically detect and restore the degradation in RRAM-based neuromorphic systems due to RRAM soft-errors. The system and circuit level techniques to implement these algorithms are also explained in this work. In conclusion, this work offers initial steps for enabling the usage of RRAM devices in products by tackling one of its most known challenges: RRAM reliability and radiation soft-errors. Despite using experimentally verified SPICE models and widely popular system simulators and test benches, the provided solutions in this thesis need to be verified in the future work through fabrication to study the impact of other RRAM technology shortcomings including: a) the instability in its switching dynamics due to the stochastic nature of oxygen vacancies movement, and b) its sensitivity to process variations

Tunnel Field Effect Transistors:from Steep-Slope Electronic Switches to Energy Efficient Logic Applications

The aim of this work has been the investigation of homo-junction Tunnel Field Effect Transistors starting from a compact modelling perspective to its possible applications. Firstly a TCAD based simulation study is done to explain the main device characteristics. The main differences of a Tunnel FET with respect to a conventional MOSFET is pointed out and the differences have been explained. A compact DC/AC model has been developed which is capable of describing the I-V characteristics in all regimes of operation. The model takes in to account ambi-polarity, drain side breakdown and all tunneling related physics. A temperature dependence is also added to the model to study the temperature independent behavior of tunneling. The model was further implemented in a Verilog-A based circuit simulator. Following calibration to experimental results of Silicon and strained-Silicon TFETs, the model has been also used to benchmark against a standard CMOS node for digital and analog applications. The circuits built with Tunnel FETs showed interesting temperature behavior which was superior to the compared CMOS node. In the same work, we also explore and propose solutions for using TFETs for low power memory applications. Both volatile and non-volatile memory concepts are investigated and explored. The application of a Tunnel FET as a capacitor-less memory has been experimentally demonstrated for the first time. New device concepts have been proposed and process flows for the same are developed to realize them in the clean room in EPFL

Nanocluster-rich SiO2 layers produced by ion beam synthesis: electrical and optoelectronic properties

The aim of this work was to find a correlation between the electrical, optical and microstructural properties of thin SiO2 layers containing group IV nanostructures produced by ion beam synthesis. The investigations were focused on two main topics: The electrical properties of Ge- and Si-rich oxide layers were studied in order to check their suitability for non-volatile memory applications. Secondly, photo- and electroluminescence (PL and EL) results of Ge-, Si/C- and Sn-rich SiO2 layers were compared to electrical properties to get a better understanding of the luminescence mechanism

Resistance switching devices based on amorphous insulator-metal thin films

Nanometallic devices based on amorphous insulator-metal thin films are developed to provide a novel non-volatile resistance-switching random-access memory (RRAM). In these devices, data recording is controlled by a bipolar voltage, which tunes electron localization length, thus resistivity, through electron trapping/detrapping. The low-resistance state is a metallic state while the high-resistance state is an insulating state, as established by conductivity studies from 2K to 300K. The material is exemplified by a Si3N4 thin film with randomly dispersed Pt or Cr. It has been extended to other materials, spanning a large library of oxide and nitride insulator films, dispersed with transition and main-group metal atoms. Nanometallic RRAMs have superior properties that set them apart from other RRAMs. The critical switching voltage is independent of the film thickness/device area/temperature/switching speed. Trapped electrons are relaxed by electron-phonon interaction, adding stability which enables long-term memory retention. As electron-phonon interaction is mechanically altered, trapped electron can be destabilized, and sub-picosecond switching has been demonstrated using an electromagnetically generated stress pulse. AC impedance spectroscopy confirms the resistance state is spatially uniform, providing a capacitance that linearly scales with area and inversely scales with thickness. The spatial uniformity is also manifested in outstanding uniformity of switching properties. Device degradation, due to moisture, electrode oxidation and dielectrophoresis, is minimal when dense thin films are used or when a hermetic seal is provided. The potential for low power operation, multi-bit storage and complementary stacking have been demonstrated in various RRAM configurations.Comment: 523 pages, 215 figures, 10 chapter

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

Design and analysis of memristor-based reliable crossbar architectures

The conventional transistor-based computing landscape is already undergoing dramatic changes. While transistor-based devices’ scaling is approaching its physical limits in nanometer technologies, memristive technologies hold the potential to scale to much smaller geometries. Memristive devices are used majorly in memory design but they also have unignorable applications in logic design, neuromorphic computing, sensors among many others. The most critical research and development problems that must be resolved before memristive architectures become mainstream are related to their reliability. One of such reliability issue is the sneak-paths current which limits the maximum crossbar array size. This thesis presents various designs of the memristor based crossbar architecture and corresponding experimental analysis towards addressing its reliability issues. Novel contribution of this thesis starts with the formulation of robust analytic models for read and write schemes used in memristive crossbar arrays. These novel models are less restrictive and are suitable for accurate mathematical analysis of any mn crossbar array and the evaluation of their performance during these critical operations. In order to minimise the sneak-paths problem, we propose techniques and conditions for reliable read operations using simultaneous access of multiple bits in the crossbar array. Two new write techniques are also presented, one to minimise failure during single cell write and the other designed for multiple cells write operation. Experimental results prove that the single write technique minimises write voltage drop degradation compared to existing techniques. Test results from the multiple cells write technique show it consumes less power than other techniques depending on the chosen configuration. Lastly, a novel Verilog-A memristor model for simulation and analysis of memristor’s application in gas sensing is presented. This proposed model captures the gas sensing properties of titanium-dioxide using gas concentration to control the overall memristance of the device. This model is used to design and simulate a first-of-its-kind sneak-paths free memristor-based gas detection arrays. Experimental results from a 88 memristor sensor array show that there is a ten fold improvement in the accuracy of the sensor’s response when compared with a single memristor sensor