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

Adaptation in Standard CMOS Processes with Floating Gate Structures and Techniques

We apply adaptation into ordinary circuits and systems to achieve high performance, high quality results. Mismatch in manufactured VLSI devices has been the main limiting factor in quality for many analog and mixed-signal designs. Traditional compensation methods are generally costly. A few examples include enlarging the device size, averaging signals, and trimming with laser. By applying floating gate adaptation to standard CMOS circuits, we demonstrate here that we are able to trim CMOS comparator offset to a precision of 0.7mV, reduce CMOS image sensor fixed-pattern noise power by a factor of 100, and achieve 5.8 effective number of bits (ENOB) in a 6-bit flash analog-to-digital converter (ADC) operating at 750MHz. The adaptive circuits generally exhibit special features in addition to an improved performance. These special features are generally beyond the capabilities of traditional CMOS design approaches and they open exciting opportunities in novel circuit designs. Specifically, the adaptive comparator has the ability to store an accurate arbitrary offset, the image sensor can be set up to memorize previously captured scenes like a human retina, and the ADC can be configured to adapt to the incoming analog signal distribution and perform an efficient signal conversion that minimizes distortion and maximizes output entropy

Utilizing Magnetic Tunnel Junction Devices in Digital Systems

The research described in this dissertation is motivated by the desire to effectively utilize magnetic tunnel junctions (MTJs) in digital systems. We explore two aspects of this: (1) a read circuit useful for global clocking and magnetologic, and (2) hardware virtualization that utilizes the deeply-pipelined nature of magnetologic. In the first aspect, a read circuit is used to sense the state of an MTJ (low or high resistance) and produce a logic output that represents this state. With global clocking, an external magnetic field combined with on-chip MTJs is used as an alternative mechanism for distributing the clock signal across the chip. With magnetologic, logic is evaluated with MTJs that must be sensed by a read circuit and used to drive downstream logic. For these two uses, we develop a resistance-to-voltage (R2V) read circuit to sense MTJ resistance and produce a logic voltage output. We design and fabricate a prototype test chip in the 3 metal 2 poly 0.5 um process for testing the R2V read circuit and experimentally validating its correctness. Using a clocked low/high resistor pair, we show that the read circuit can correctly detect the input resistance and produce the desired square wave output. The read circuit speed is measured to operate correctly up to 48 MHz. The input node is relatively insensitive to node capacitance and can handle up to 10s of pF of capacitance without changing the bandwidth of the circuit. In the second aspect, hardware virtualization is a technique by which deeply-pipelined circuits that have feedback can be utilized. MTJs have the potential to act as state in a magnetologic circuit which may result in a deep pipeline. Streams of computation are then context switched into the hardware logic, allowing them to share hardware resources and more fully utilize the pipeline stages of the logic. While applicable to magnetologic using MTJs, virtualization is also applicable to traditional logic technologies like CMOS. Our investigation targets MTJs, FPGAs, and ASICs. We develop M/D/1 and M/G/1 queueing models of the performance of virtualized hardware with secondary memory using a fixed, hierarchical, round-robin schedule that predict average throughput, latency, and queue occupancy in the system. We develop three C-slow applications and calibrate them to a clock and resource model for FPGA and ASIC technologies. Last, using the M/G/1 model, we predict throughput, latency, and resource usage for MTJ, FPGA, and ASIC technologies. We show three design scenarios illustrating ways in which to use the model

Design of Logic-Compatible Embedded Flash Memories for Moderate Density On-Chip Non-Volatile Memory Applications

University of Minnesota Ph.D. dissertation. December 2013. Major: Electrical Engineering. Advisor: Chris H. Kim. 1 computer file (PDF); xx, 129 pages.An on-chip embedded NVM (eNVM) enables a zero-standby power system-on-a-chip with a smaller form factor, faster access speed, lower access power, and higher security than an off-chip NVM. Differently from the high density eNVM technologies such as dual-poly eflash, FeRAM, STT-MRAM, and RRAM that typically require process overhead beyond standard logic process, the moderate density eNVM technologies such as e-fuse, anti-fuse, and single-poly embedded flash (eflash) can be fabricated in a standard logic process with no process overhead. Among them, a single-poly eflash is a unique multiple-time programmable moderate density eNVM, while it is expected to play a key role in mitigating variability and reliability issues of the future VLSI technologies; however, the challenges such as a high voltage disturbance, an implementation of logic compatible High Voltage Switch (HVS), and a limited sensing margin are required to be solved for its implementation using a standard I/O device. This thesis focuses on alleviating such challenges of the single-poly eflash memory with three single-poly eflash designs proposed in a generic logic process for moderate density eNVM applications. Firstly, the proposed 5T eflash features a WL-by-WL accessible architecture with no disturbance issue of the unselected WL cells, an overstress-free multi-story HVS expanding the cell sensing margin, and a selective WL refresh scheme for the higher cell endurance. The most favorable eflash cell configuration is also studied when the performance, endurance, retention, and disturbance characteristics are all considered. Secondly, the proposed 6T eflash features the bit-by-bit re-write capability for the higher overall cell endurance, while not disturbing the unselected WL cells. The logic compatible on-chip charge pump to provide the appropriate high voltages for the proposed eflash operations is also discussed. Finally, the proposed 10T eflash features a multi-configurable HVS that does not require the boosted read supplies, and a differential cell architecture with improved retention time. All these proposed eflash memories were implemented in a 65nm standard logic process, and the test chip measurement results confirmed the functionality of the proposed designs with a reasonable retention margin, showing the competitiveness of the proposed eflash memories compared to the other moderate density eNVM candidates

Low Power Circuits for Miniature Sensor Systems.

With the development of VLSI technologies, the sensor systems of all kinds of applications have entered our everyday's life. For specific applications such as medical implants, the form factor of such systems is the crucial concern. In order to minimize of size of the power sources with a given lifetime, the ability to operate the system with low power consumption is the key. An effective way of lowering the active power dissipation is through aggressive voltage scaling. For minimal energy operation, the optimum supply voltage is typical lower than the subthreshold voltage. On the other hand, a sensor system spends most of the time idling while only actively obtaining data in a short period of time. As a result, strong power gating is needed for reducing the leakage power. We discuss the design challenges for several building blocks for the sensor system that have not been gotten much emphasis in term of power consumption. To monitor the period for idle time and to wake up the system periodically, two types of ultra low power timers are proposed. The first one utilizes the gate leakage of a MOS transistor to achieve low temperature dependency and large time constant. The second one implements a program-and-hold technique to compensate for the temperature coefficient of a one-shot oscillator with 150pW of average power. We propose a low power temperature sensor that is suitable for passive RFID transponder. To retrieve the data out of the sensor chip, two passive proximity communication schemes are presented. Capacitive coupling can be used for chips on a stack where the key challenge is misalignment. A alignment detection and microplate reconfiguration method is proposed to solve the problem. We also propose a passive inductive coupling scheme using pulse signaling. Compared to the traditional backscattering technique, the limitations on the quality factor of the inductor and the signal sensitivity of the receiver can be relaxed.PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/61782/1/yushiang_1.pd

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

Efficient Neuromorphic Computing Enabled by Spin-Transfer Torque: Devices, Circuits and Systems

Present day computers expend orders of magnitude more computational resources to perform various cognitive and perception related tasks that humans routinely perform everyday. This has recently resulted in a seismic shift in the field of computation where research efforts are being directed to develop a neurocomputer that attempts to mimic the human brain by nanoelectronic components and thereby harness its efficiency in recognition problems. Bridging the gap between neuroscience and nanoelectronics, this thesis demonstrates the encoding of biological neural and synaptic functionalities in the underlying physics of electron spin. Description of various spin-transfer torque mechanisms that can be potentially utilized for realizing neuro-mimetic device structures is provided. A cross-layer perspective extending from the device to the circuit and system level is presented to envision the design of an All-Spin neuromorphic processor enabled with on-chip learning functionalities. Device-circuit-algorithm co-simulation framework calibrated to experimental results suggest that such All-Spin neuromorphic systems can potentially achieve almost two orders of magnitude energy improvement in comparison to state-of-the-art CMOS implementations