Integrated Circuits for Programming Flash Memories in Portable Applications

Smart devices such as smart grids, smart home devices, etc. are infrastructure systems that connect the world around us more than before. These devices can communicate with each other and help us manage our environment. This concept is called the Internet of Things (IoT). Not many smart nodes exist that are both low-power and programmable. Floating-gate (FG) transistors could be used to create adaptive sensor nodes by providing programmable bias currents. FG transistors are mostly used in digital applications like Flash memories. However, FG transistors can be used in analog applications, too. Unfortunately, due to the expensive infrastructure required for programming these transistors, they have not been economical to be used in portable applications. In this work, we present low-power approaches to programming FG transistors which make them a good candidate to be employed in future wireless sensor nodes and portable systems. First, we focus on the design of low-power circuits which can be used in programming the FG transistors such as high-voltage charge pumps, low-drop-out regulators, and voltage reference cells. Then, to achieve the goal of reducing the power consumption in programmable sensor nodes and reducing the programming infrastructure, we present a method to program FG transistors using negative voltages. We also present charge-pump structures to generate the necessary negative voltages for programming in this new configuration

Potential and Challenges of Analog Reconfigurable Computation in Modern and Future CMOS

In this work, the feasibility of the floating-gate technology in analog computing platforms in a scaled down general-purpose CMOS technology is considered. When the technology is scaled down the performance of analog circuits tends to get worse because the process parameters are optimized for digital transistors and the scaling involves the reduction of supply voltages. Generally, the challenge in analog circuit design is that all salient design metrics such as power, area, bandwidth and accuracy are interrelated. Furthermore, poor flexibility, i.e. lack of reconfigurability, the reuse of IP etc., can be considered the most severe weakness of analog hardware. On this account, digital calibration schemes are often required for improved performance or yield enhancement, whereas high flexibility/reconfigurability can not be easily achieved. Here, it is discussed whether it is possible to work around these obstacles by using floating-gate transistors (FGTs), and analyze problems associated with the practical implementation. FGT technology is attractive because it is electrically programmable and also features a charge-based built-in non-volatile memory. Apart from being ideal for canceling the circuit non-idealities due to process variations, the FGTs can also be used as computational or adaptive elements in analog circuits. The nominal gate oxide thickness in the deep sub-micron (DSM) processes is too thin to support robust charge retention and consequently the FGT becomes leaky. In principle, non-leaky FGTs can be implemented in a scaled down process without any special masks by using “double”-oxide transistors intended for providing devices that operate with higher supply voltages than general purpose devices. However, in practice the technology scaling poses several challenges which are addressed in this thesis. To provide a sufficiently wide-ranging survey, six prototype chips with varying complexity were implemented in four different DSM process nodes and investigated from this perspective. The focus is on non-leaky FGTs, but the presented autozeroing floating-gate amplifier (AFGA) demonstrates that leaky FGTs may also find a use. The simplest test structures contain only a few transistors, whereas the most complex experimental chip is an implementation of a spiking neural network (SNN) which comprises thousands of active and passive devices. More precisely, it is a fully connected (256 FGT synapses) two-layer spiking neural network (SNN), where the adaptive properties of FGT are taken advantage of. A compact realization of Spike Timing Dependent Plasticity (STDP) within the SNN is one of the key contributions of this thesis. Finally, the considerations in this thesis extend beyond CMOS to emerging nanodevices. To this end, one promising emerging nanoscale circuit element - memristor - is reviewed and its applicability for analog processing is considered. Furthermore, it is discussed how the FGT technology can be used to prototype computation paradigms compatible with these emerging two-terminal nanoscale devices in a mature and widely available CMOS technology.Siirretty Doriast

High Frequency Devices and Circuit Modules for Biochemical Microsystems

This dissertation investigates high frequency devices and circuit modules for biochemical microsystems. These modules are designed towards replacing external bulky laboratory instruments and integrating with biochemical microsystems to generate and analyze signals in frequency and time domain. The first is a charge pump circuit with modified triple well diodes, which is used as an on-chip power supply. The second is an on-chip pulse generation circuit to generate high voltage short pulses. It includes a pulse-forming-line (PFL) based pulse generation circuit, a Marx generator and a Blumlein generator. The third is a six-port circuit based on four quadrature hybrids with 2.0~6.0 GHz operating frequency tuning range for analyzing signals in frequency domain on-chip. The fourth is a high-speed sample-and-hold circuit (SHC) with a 13.3 Gs/s sampling rate and ~11.5 GHz input bandwidth for analyzing signals in time domain on-chip. The fifth is a novel electron spin resonance (ESR) spectroscopy with high-sensitivity and wide frequency tuning range

Programming of Floating-Gate Transistors for Nonvolatile Analog Memory Array

Since they were introduced, floating-gate (FG) transistors have been used as non-volatile digital memory. Recent research has shown that floating-gate transistors can be successfully used as analog memory, specifically as programmable voltage and current sources. However, their proliferation has been limited due to the complex programming procedure and the complex testing equipment. Analog applications such as field-programmable analog arrays (FPAAs) require hundreds to thousands of floating-gate transistors on a single chip which makes the programming process even more complicated and very challenging. Therefore, a simplified, compact, and low-power scheme to program FGs are necessary. This work presents an improved version of the typical methodology for FG programming. Additionally, a novel programming methodology that utilizes negative voltages is presented here. This method simplifies the programming process by eliminating the use of supplementary and complicated infrastructure circuits, which makes the FG transistor a good candidate for low-power wireless sensor nodes and portable systems

A Parallel Programmer for Non-Volatile Analog Memory Arrays

Since their introduction in 1967, floating-gate transistors have enjoyed widespread success as non-volatile digital memory elements in EEPROM and flash memory. In recent decades, however, a renewed interest in floating-gate transistors has focused on their viability as non-volatile analog memory, as well as programmable voltage and current sources. They have been used extensively in this capacity to solve traditional problems associated with analog circuit design, such as to correct for fabrication mismatch, to reduce comparator offset, and for amplifier auto-zeroing. They have also been used to implement adaptive circuits, learning systems, and reconfigurable systems. Despite these applications, their proliferation has been limited by complex programming procedures, which typically require high-precision test equipment and intimate knowledge of the programmer circuit to perform.;This work strives to alleviate this limitation by presenting an improved method for fast and accurate programming of floating-gate transistors. This novel programming circuit uses a digital-to-analog converter and an array of sample-and-hold circuits to facilitate fast parallel programming of floating-gate memory arrays and eliminate the need for high accuracy voltage sources. Additionally, this circuit employs a serial peripheral interface which digitizes control of the programmer, simplifying the programming procedure and enabling the implementation of software applications that obscure programming complexity from the end user. The efficient and simple parallel programming system was fabricated in a 0.5?m standard CMOS process and will be used to demonstrate the effectiveness of this new method

An Analog VLSI Deep Machine Learning Implementation

Machine learning systems provide automated data processing and see a wide range of applications. Direct processing of raw high-dimensional data such as images and video by machine learning systems is impractical both due to prohibitive power consumption and the “curse of dimensionality,” which makes learning tasks exponentially more difficult as dimension increases. Deep machine learning (DML) mimics the hierarchical presentation of information in the human brain to achieve robust automated feature extraction, reducing the dimension of such data. However, the computational complexity of DML systems limits large-scale implementations in standard digital computers. Custom analog signal processing (ASP) can yield much higher energy efficiency than digital signal processing (DSP), presenting means of overcoming these limitations. The purpose of this work is to develop an analog implementation of DML system. First, an analog memory is proposed as an essential component of the learning systems. It uses the charge trapped on the floating gate to store analog value in a non-volatile way. The memory is compatible with standard digital CMOS process and allows random-accessible bi-directional updates without the need for on-chip charge pump or high voltage switch. Second, architecture and circuits are developed to realize an online k-means clustering algorithm in analog signal processing. It achieves automatic recognition of underlying data pattern and online extraction of data statistical parameters. This unsupervised learning system constitutes the computation node in the deep machine learning hierarchy. Third, a 3-layer, 7-node analog deep machine learning engine is designed featuring online unsupervised trainability and non-volatile floating-gate analog storage. It utilizes massively parallel reconfigurable current-mode analog architecture to realize efficient computation. And algorithm-level feedback is leveraged to provide robustness to circuit imperfections in analog signal processing. At a processing speed of 8300 input vectors per second, it achieves 1×1012 operation per second per Watt of peak energy efficiency. In addition, an ultra-low-power tunable bump circuit is presented to provide similarity measures in analog signal processing. It incorporates a novel wide-input-range tunable pseudo-differential transconductor. The circuit demonstrates tunability of bump center, width and height with a power consumption significantly lower than previous works

ELECTRICAL CHARACTERIZATION, PHYSICS, MODELING AND RELIABILITY OF INNOVATIVE NON-VOLATILE MEMORIES

Enclosed in this thesis work it can be found the results of a three years long research activity performed during the XXIV-th cycle of the Ph.D. school in Engineering Science of the Università degli Studi di Ferrara. The topic of this work is concerned about the electrical characterization, physics, modeling and reliability of innovative non-volatile memories, addressing most of the proposed alternative to the floating-gate based memories which currently are facing a technology dead end. Throughout the chapters of this thesis it will be provided a detailed characterization of the envisioned replacements for the common NOR and NAND Flash technologies into the near future embedded and MPSoCs (Multi Processing System on Chip) systems. In Chapter 1 it will be introduced the non-volatile memory technology with direct reference on nowadays Flash mainstream, providing indications and comments on why the system designers should be forced to change the approach to new memory concepts. In Chapter 2 it will be presented one of the most studied post-floating gate memory technology for MPSoCs: the Phase Change Memory. The results of an extensive electrical characterization performed on these devices led to important discoveries such as the kinematics of the erase operation and potential reliability threats in memory operations. A modeling framework has been developed to support the experimental results and to validate them on projected scaled technology. In Chapter 3 an embedded memory for automotive environment will be shown: the SimpleEE p-channel memory. The characterization of this memory proven the technology robustness providing at the same time new insights on the erratic bits phenomenon largely studied on NOR and NAND counterparts. Chapter 4 will show the research studies performed on a memory device based on the Nano-MEMS concept. This particular memory generation proves to be integrated in very harsh environment such as military applications, geothermal and space avionics. A detailed study on the physical principles underlying this memory will be presented. In Chapter 5 a successor of the standard NAND Flash will be analyzed: the Charge Trapping NAND. This kind of memory shares the same principles of the traditional floating gate technology except for the storage medium which now has been substituted by a discrete nature storage (i.e. silicon nitride traps). The conclusions and the results summary for each memory technology will be provided in Chapter 6. Finally, on Appendix A it will be shown the results of a recently started research activity on the high level reliability memory management exploiting the results of the studies for Phase Change Memories