The increasing demand for energy-efficient computing has created the need for advanced power management solutions. Backside power delivery network (BSPDN) has been introduced in the industry for 2-nm node with passive wires. In this work, we propose adding active components (power transistors) to the backside of silicon in a back-end-of-line (BEOL)-compatible fabrication process. The goal is to enable 12–0.7-V voltage downconversion at the backside of silicon (near the point of load, i.e., the frontside logic compute die) to minimize the IR drop and improve overall system-level conversion efficiency. This work leverages a hybrid monolithic 3-D (M3D)dc-dc converter architecture combining switched-capacitor (SC) and synchronous buck converter topologies with BEOL-compatible active and passive devices. The design employs amorphous tungsten-doped indium oxide (IWO) transistors, which offer high breakdown voltage and tunable threshold voltages, supporting both enhancement and depletion modes for efficient switching. With the experimentally calibrated compact models, the simulated hybrid converter design achieves 12–0.7-V conversion with a peak efficiency of 95.6% at a power density of 330 mW/mm2, demonstrating the feasibility of M3D SC dc-dc converters for next-generation power management in high-performance edge devices.

Cryogenic CMOS is a promising technology for high performance computing due to its improvement in subthreshold slope, carrier mobilities and reduced wire resistance. The threshold voltage (Vth) increase at 77K can be mitigated by metal gate work function (PHIG) engineering to achieve matched off current (Ioff) further enhancing the device performance allowing us to operate at very low supply voltage thereby reducing the Energy Delay Product (EDP). However, the effect of variation on noise margins of static random access memories (SRAM) deploying these matched Ioff devices is very prominent especially at low supply voltages (Vdd) limiting its scaling. In this work, we propose a framework to perform Vth retargeting for cryogenic SRAM for improving noise margins in high performance cryogenic SRAM cells under variation. The proposed framework comprises of a Monte-Carlo engine which performs statistical analysis and DC characterization and a backend processing engine to analyze noise margins and tune the PHIG. To demonstrate the framework, we use calibrated 14nm FinFET models at 300K and 77K. First, we analyze the logic blocks using iso-Ioff devices, which yield up to 3x improvement in delay at iso-energy and a 4.5x reduction in energy at iso-delay. Next, we study the effect of Vth variation on the device currents. Finally, the framework is deployed to tune PHIG, and results show that it can enhance the noise margins by 23%, 31% and 19% for hold, read and write operations respectively at 77K compared to iso-Ioff devices. Further, a 1kb SRAM array has been simulated using iso-Ioff tuned peripherals and framework tuned SRAM cells, and it shows 5.4x reduction in read/write energies along with 1.2x delay reduction and better noise margins at 77K compared to 300K.

This paper introduces a Computing-In-Memory based Spiking Neural Network (SNN) architecture for cryogenic operation of CMOS (Cryo-SNN). The paper demonstrates design strategies to improve energy efficiency of Cryo-SNN by coupling low-voltage operation at cryogenic temperature with innovative design of neuron circuits optimized for cryogenic conditions. By exploiting the enhanced device characteristics of 14 nm FinFET transistors at cryogenic temperatures, our architecture outlines critical adaptations to SNN components for optimal functionality in extreme environments. The circuit simulation using measurement calibrated 14nm FinFET models shows that a Cryo-SNN designed for MNIST classification operates with 4.54X improved energy-delay-product (EDP) over room temperature operation while maintaining similar accuracy. Further, the paper designs an optimized SNN architecture for autonomous health monitoring of miniaturized satellites at cryogenic temperature consuming less than 1mW of power.

Cryogenic High-Performance Computing (HPC) has gained traction for server and cloud systems which demand large scale, energy efficient and fast computing systems. Dynamic logic satisfies these goals and at cryogenic temperature, its inherent problems of charge leakage are readily addressed thanks to the exponential reduction in subthreshold leakage currents. Fully Depleted Silicon on Insulator (FDSOI) devices present an additional “dial” of back gate biasing which opens multiple design options and solutions to further enhance the circuit power performance metrics. In this paper we present a solution - selective back gate biasing ― applied to dynamic and domino logic circuits, to increase their energy efficiency and/or performance. With the proposed method, we show up to 48% decrease in delay at constant energy and 41% decrease in energy at constant delay at 77K compared to 300K. We further scale up the circuit to a radix-4 sparse-2 64 bit adder where the proposed technique increases energy efficiency by 53% and/or performance by 56% going from 300K to 77K.

With Copper (Cu) Interconnects causing performance bottleneck at single nanometer nodes due to increase in resistivity size effects viz., grain boundary scattering and surface scattering, there has always been scavenging for alternate interconnect materials. Although the Cu resistivity value decreases at cryogenic temperature, the problems continue to persist. In this work, we study three alternate interconnect materials specifically for 77K High Performance Compute applications. We select the materials based on their resistivity value at 77K for 7nm node computed using Fuchs-Sondheimer-Mayadas-Shatzkes (FS-MS) models. We analyze the delay of the interconnects, understand repeater insertion as a function of wire length, evaluate repeater count and energy at system level and perform IR drop analysis by showing through detailed analytical models that Ru, Rh and Al can provide appreciable improvements over Cu at 77K. The delay of interconnects reduces by 1-3.75% for Ru, 1.5-7.25% for Rh and 4.4-17.8% for Al across the BEOL stack while repeater counts decrease by 10%, 15% and 37% for Ru, Rh and Al respectively at 77K. We investigate thermal and reliability aspects of interconnect design including electromigration, Joule Heating and maximum allowed current densities again proving that Ru (9%), Rh (18%) and Al (63%) outperform Cu at 77K. Finally, we study the effects of various Low-k dielectric materials on the interconnect capacitance and thermal behavior for Cu as well as three alternate materials noting that, even though thermal conductivity of dielectrics decrease at 77K, the Joule Heating will not be as worse as one might expect.

Compute-in-memory has received a lot of research interests recently to implement the data-intensive computation in deep neural networks. By performing the computing at the storage location, CIM avoids the excessive data transfer thus improving the energy efficiency. SRAM based CIM is one of the promising candidates for its mature technology availability at advanced technology node. To further speed up for CMOS circuits, cryogenic computing which operates at low temperatures has emerged as an attractive solution for high-performance computing at the data center. In this work, we modified NeuroSim, a device-to-system modelling framework with experimentally calibrated 28nm transistor parameters from room temperature to 4K Then we benchmark the performance of SRAM based CIM for ResNet-18 on ImagNet dataset. The energy-delay-product is compared across the temperature, revealing the performance and energy efficiency boost by cryogenic computing. When the cooling infrastructure cost is considered, the overall energy benefits are overshadowed though.

Research discovery of ferroelectricity in doped hafnium dioxide thin films has ignited tremendous activity in exploration of ferroelectric FETs for a range of applications from low-power logic to embedded non-volatile memory to in-memory compute kernels. In this paper, key milestones in the evolution of Ferroelectric Field Effect Transistors (FeFETs) and the emergence of a versatile ferroelectronic platform are presented. FeFET exhibits superior energy efficiency and high performance as embedded nonvolatile memory. When embedded into logic, such as SRAM or D-flip-flop, nonvolatile processor can be designed, which is critical for intermittent computing with unreliable power. The partial polarization switching in multi-domain ferroelectric can be harnessed to develop analog synaptic weight cell for deep learning accelerators. To further improve the energy-efficiency of computation, ferroelectric in-memory computing hardware primitive is designed, with one prominent example of ferroelectric TCAM. Utilizing the ferroelectric switching dynamics, ferroelectric neuron with intrinsic homeostasis can be realized to enable a unified ferroelectric platform for spiking neural network. From all these developments, ferroelectric emerges as a highly promising platform for various exciting applications.

An analog synapse circuit based on ferroelectric-metal field-effect transistors is proposed, that offers 6-bit weight precision. The circuit is comprised of volatile least significant bits (LSBs) used solely during training, and non-volatile most significant bits (MSBs) used for both training and inference. The design works at a 1.8V logic-compatible voltage, provides 1010endurance cycles, and requires only 250ps update pulses. A variant of LeNet trained with the proposed synapse achieves 98.2% accuracy on MNIST, which is only 0.4% lower than an ideal implementation of the same network with the same bit precision. Furthermore, the proposed synapse offers improvements of up to 26% in area, 44.8% in leakage power, 16.7% in LSB update pulse duration, and two orders of magnitude in endurance cycles, when compared to state-of-the-art hybrid synaptic circuits. Our proposed synapse can be extended to an 8-bit design, enabling a VGG-like network to achieve 88.8% accuracy on CIFAR-10 (only 0.8% lower than an ideal implementation of the same network).

Innovative and new computing paradigms must be considered as we reach the limits of von Neumann computing caused by the growth in necessary data processing. This paper provides an introduction to three emerging computing models that have established themselves as likely post-CMOS and post-von Neumann solutions. The first of these ideas is quantum computing, for which we discuss the challenges and potential of quantum computer architectures. Next, a computational system using intrinsic oscillators is introduced and an example is provided which shows its superiority in comparison to a typical von Neumann computational system. Finally, digital memcomputing using self-organizing logic gates is explained and then discussed as a method for optimization problems and machine learning.

Multiplicative stochasticity such as Dropout improves the robustness and gener- alizability deep neural networks. Here, we further demonstrate that always-on multiplicative stochasticity combined with simple threshold neurons provide a suf- ficient substrate for deep learning machines. We call such models Neural Sampling Machines (NSM). We find that the probability of activation of the NSM exhibits a self-normalizing property that mirrors Weight Normalization, a previously studied mechanism that fulfills many of the features of Batch Normalization in an online fashion. The normalization of activities during training speeds up convergence by preventing internal covariate shift caused by changes in the distribution of inputs. The always-on stochasticity of the NSM confers the following advantages: the network is identical in the inference and learning phases, making the NSM a suitable substrate for continual learning, it can exploit stochasticity inherent to a physical substrate such as analog non-volatile memories for in memory computing, and it is suitable for Monte Carlo sampling, while requiring almost exclusively addition and comparison operations. We demonstrate NSMs on standard classification benchmarks (MNIST and CIFAR) and event-based classification benchmarks (N-MNIST and DVS Gestures). Our results show that NSMs perform comparably or better than conventional artificial neural networks with the same architecture.

As we approach the end of the silicon road map, alternative computing models that can solve at-scale problems in the data-centric world are becoming important. This is accompanied by the realization that binary abstraction and Boolean logic, which have been the foundations of modern computing revolution, fall short of the desired performance and power efficiency. In particular, hard computing problems relevant to pattern matching, image and signal processing, optimizations, and neuromorphic applications require alternative approaches. In this paper, we review recent advances in oscillatory dynamical system-based models of computing and their implementations. We show that simple configurations of oscillators connected using simple electrical circuits can result in interesting phase and frequency dynamics of such coupled oscillatory systems. Such networks can be controlled, programmed, and observed to solve computationally hard problems. Although our discussion in this paper is limited to insulator-to-metal transition devices and spin-torque oscillators, the general philosophy of such a computing paradigm of “let physics do the computing” can be translated to other mediums as well, including micromechanical and optical systems. We present an overview of the mathematical treatments necessary to understand the time evolution of these systems and highlight the recent experimental results in this area that suggest the potential of such computational models.

Negative-capacitance FETs (NCFETs) are a promising candidate for low-power circuits with intrinsic features, e.g., the steep switching slope. Prior works have shown potential for enabling low-power digital logic and memory design with NCFETs. Yet, it is still not quite clear how to harness these new features of NCFETs for analog functionalities. This article provides more insights into the circuit design space with new device characteristics and investigates its deployment in analog circuits, specifically, time-domain analog-to-digital converters (ADCs) and phase-locked loops (PLLs). We propose and optimize a novel digital-based clocked comparator and a capacitor-based voltage-to-time converter (VTC), which are essential building blocks in ADCs and PLLs. Evaluation results show beyond-FinFET comparison speed and enhanced linearity for the proposed NCFET-based clocked comparator and VTC, respectively. Such improvement is achieved by exploiting the steeper slope and increased output impedance of NCFETs. More details on design details and a discussion are provided in this article.

In this paper, we consider devices, circuits, and systems comprised of transistors with integrated ferroelectrics. Said structures are actively being considered by various semiconductor manufacturers as they can address a large and unique design space. Transistors with integrated ferroelectrics could (i) enable a better switch (i.e., offer steeper subthreshold swings), (ii) are CMOS compatible, (iii) have multiple operating modes (i.e., I-V characteristics can also enable compact, 1-transistor, non-volatile storage elements, as well as analog synaptic behavior), and (iv) have been experimentally demonstrated (i.e., with respect to all of the aforementioned operating modes). These device-level characteristics offer unique opportunities at the circuit, architectural, and system-level, and are considered here from device, circuit/architecture, and foundry-level perspectives.

This paper will give a review of recent work on using networks of coupled oscillators for analog information processing. We will discuss the rationale of using coupled oscillators, and how they can be used to perform computational tasks, such as associative computing primitives, or how they can serve as hardware accelerators in vision processing pipelines. Further, we will study two specific physical implementations for such oscillator, namely relaxation oscillators based on metal-insulator phase transitions and magnetic spin-torque oscillators. We will also discuss the potential of such coupled-oscillator networks to solve computationally-hard optimization problems or even NP-hard problems.

Conventional CMOS scaling and the Moore's law have been the cornerstone of progress in computing hardware technology. However, with dimensional scaling expected to end soon, there is a pressing need to find the next information processing hardware that can continue to support the technology revolution. Will this hardware solution be an enhanced or an augmented version of MOSFET or a switch based on a radically new switching mechanism. Ultimately, do we require a complete deviation from the Boolean paradigm itself? In this invited paper, we will review some of the actively pursued future logic, merged logic-memory and related concepts.

This paper reviews an analog circuit system of capacitively coupled relaxation oscillators whose time evolution can be used to solve the graph coloring problem. These oscillators consist of a series combination of an insulator-metal-transition (IMT) device and a resistance. Such circuits were also demonstrated experimentally using VO2(Vanadium Dioxide) as the phase transition material. The time evolution of circuit dynamics depend on eigenvectors of the adjacency matrix in the same way as is used by spectral algorithms for graph coloring. As such, a coupled network of such oscillators with piecewise linear dynamics have steady state phases which can be used to approximate the minimum vertex coloring of a graph.

In this paper we review recent work on computational paradigms involving coupled relaxation oscillators built using metal-insulator-transition (MIT) devices. Such oscillators made using MIT devices based on Vanadium-Dioxide thin films are very compact and can be realized in hardware. Networks of such oscillators have interesting phase and frequency dynamics which can be programmed to solve computationally hard problems.

Single-electron transistor (SET) at room temperature has been demonstrated as a promising device for extending Moore's law due to its ultralow-power consumption. Previous works proposed mapping approaches to implement Boolean functions on SET arrays. However, these approaches were based on an ideal assumption that the SET arrays are defect-free. Recently, a diagnosis method was proposed targeting at defective SET arrays. However, the approach was static, such that the performance is inefficient. As a result, in this paper, we propose a dynamic diagnosis approach that can efficiently identify the locations and the types of the defects in the SET arrays. The experimental results show that the proposed dynamic diagnosis approach can achieve the same results as the previous work with much less CPU time on a set of benchmarks. Furthermore, the proposed method spent a few seconds while the previous work exceeded the CPU time limit of 3600 s on some benchmarks.

Ferroelectric FETs (FEFETs) offer intriguing possibilities for the design of low power nonvolatile memories by virtue of their three-terminal structure coupled with the ability of the ferroelectric (FE) material to retain its polarization in the absence of an electric field. Utilizing the distinct features of FEFETs, we propose a 2-transistor (2T) FEFET-based nonvolatile memory with separate read and write paths. With proper co-design at the device, cell and array levels, the proposed design achieves non-destructive read and lower write power at iso-write speed compared to standard FERAM. In addition, the FEFET-based memory exhibits high distinguishability with six orders of magnitude difference in the read currents corresponding to the two states. Comparative analysis based on experimentally calibrated models shows significant improvement of access energy-delay. For example, at a fixed write time of 550ps, the write voltage and energy are 58.5% and 67.7% lower than FERAM, respectively. These benefits are achieved with 2.4 times the area overhead. Further exploration of the proposed FEFET memory in energy harvesting nonvolatile processors shows an average improvement of 27% in forward progress over FERAM.

Numerous research efforts are targeting new devices that could continue performance scaling trends associated with Moore's Law and/or accomplish computational tasks with less energy. One such device is the ferroelectric FET (FeFET), which offers the potential to be scaled beyond the end of the silicon roadmap as predicted by ITRS. Furthermore, the Ids vs. Vgs characteristics of FeFETs may allow a device to function as both a switch and a non-volatile storage element. We exploit this FeFET property to enable fine-grained logic-in-memory (LiM). We consider three different circuit design styles for FeFET-based LiM: complementary (differential), dynamic current mode, and dynamic logic. Our designs are compared with existing approaches for LiM (i.e., based on magnetic tunnel junctions (MTJs), CMOS, etc.) that afford the same circuit-level functionality. Assuming similar feature sizes, non-volatile FeFET-based LiM circuits are more efficient than functional equivalents based on MTJs when considering metrics such as propagation delay (2.9×, 6.8×) and dyanmic power (3.7×, 2.3×) (for 45 nm, 22 nm technology respectively). Compared to CMOS functional equivalents, FeFET designs still exhibit modest improvements in the aforementioned metrics while also offering non-volatility and reduced device count.

Sustaining of Moore's Law over the next decade will require not only continued scaling of the physical dimensions of transistors but also performance improvement and aggressive reduction in power consumption. Hetero-junction Tunnel FET (TFET) have emerged as promising transistor candidates for supply voltage scaling down to sub-0.5V due to the possibility of sub-kT/q switching without compromising on-current (Ion). Recently, n-type III-V HTFET with reasonable on-current and sub-kT/q switching at supply voltage of 0.5V have been experimentally demonstrated. However, steep switching performance of III-V HTFET till date has been limited to range of drain current (IDS) spanning over less than a decade. In this invited presentation, we will review progress in Tunnel FETs and analyze primary roadblocks in the path towards achieving steep switching performance in III-V HTFET.

Cross-point architectures are promising for designing dense memory arrays. However, sneak current paths in a cross-point array necessitates the use of non-linear selectors. In this paper, we analyze the potential of employing correlated materials exhibiting abrupt insulator-metal transitions as selectors to design cross-point memories based on magnetic tunnel junctions (MTJs). We analyze the properties of the correlated materials and co-design MTJs and the selector to optimize the energy efficiency and robustness of the memory array. Our analysis points to the need of a correlated material with a large ratio of insulator and metal resistivities along with appropriate critical currents for the phase transitions (the values of which depend on the absolute value of the resistivities). We discuss that the design constraints lead to a restriction on the range of the selector length, which is closely related to the oxide thickness of the MTJ. Comparison of the cross-point architecture with standard architecture shows the benefits in the former in terms of 7% larger sense margin and 5X higher integration density at iso-read stability. However, this comes at the cost of 2X lower write speed (due to two-cycle write) and 11%-19% increase in the read/write power (due to sneak current in the cross-point array).

We present a non-volatile flip-flop with a feature to back-up the state in a ferroelectric transistor (FEFET) during power failure or supply gating. The data is stored in the form of polarization of the ferroelectric (FE) layer in the gate stack of the FEFET. The proposed flip-flop utilizes the non-volatility of the three-terminal FEFET to optimize the data backup and restore operations. We perform an extensive device-circuit analysis to provide insights into the design of the proposed flip-flop. We discuss the optimization of the FE thickness in the gate stack of the FEFET to introduce suitable non-volatility and present the implications at the circuit level. Our analysis shows that by virtue of the three terminal structure of the FEFET and the order of magnitude difference in the current for the two polarization states, the design of the backup/restore module is considerably simplified. Compared to a FE capacitor based non-volatile flip-flop, the proposed flip-flop achieves 40%--50% smaller backup delay, 27%--40% lower backup energy, comparable restore delay and up to an order of magnitude lower restore energy. While the FE capacitor based design leads to 76% area penalty compared to a conventional (volatile) flip-flop, the proposed design incurs only 35% area overhead.

Vertical tunnel field-effect transistors (VTFETs) have been extensively explored to overcome the scaling limits and to improve on-current ( I ON ) compared to standard lateral device structures for the future technologies. The benefits in terms of reduced footprint, high I ON and feasibility of fabrication have been demonstrated in several works. Among various VTFETs, the asymmetric heterojunction vertical tunnel FETs (HVTFETs) have emerged as one of the promising alternatives to standard transistors for low-voltage applications. However, while such device-level benefits without parasitics have been widely investigated, logic-gate design with parasitics and layout implications are not clear. In this article, we investigate and compare the layouts and parasitic capacitances and resistances of HVTFETs with FinFETs. Due to the vertical device structure of HVTFETs, a smaller footprint is observed compared to FinFETs in cells with small fan-in. However, for high fan-in cells, HVTFETs exhibit area overheads due to infeasibility of contact sharing in parallel and series transistors. These area overheads also lead to approximately 48% higher parasitic capacitance and resistance compared to FinFETs when the number of parallel and series connections increases. Further, in order to analyze the impact of parasitics, we modeled the analytical parasitics in SPICE. The models for both HVTFETs and FinFETs with parasitics were used to simulate a 15-stage inverter-based ring oscillator (RO) in order to compare the delay and energy. Our simulation results clearly show that HVTFETs exhibit less delay at a V DD < 0.45 V and higher energy efficiency for V DDs in the range of 0.3V--0.7V, albeit at the cost of 8% performance degradation.

Single-electron transistor (SET) at room temperature has been demonstrated as a promising device for extending Moore’s law due to its ultra-low power consumption. Existing SET synthesis methods synthesize a Boolean network into a large reconfigurable SET array where the height of SET array equals the number of primary inputs. However, recent experiments on device level have shown that this height is restricted to a small number, say, 10, rather than arbitrary value due to the ultra-low driving strength of SET devices. On the other hand, the width of an SET array is also suggested to be a small value. Consequently, it is necessary to decompose a large SET array into a set of small SET arrays where each of them realizes a sub-function of the original circuit with no more than 10 inputs. Thus, this article presents two techniques for achieving area-efficient SET array decomposition: One is a width minimization algorithm for reducing the area of a single SET array; the other is a depth-bounded mapping algorithm, which decomposes a Boolean network into many sub-functions such that the widths of the corresponding SET arrays are balanced. The width minimization algorithm leads to a 25%--41% improvement compared to the state of the art, and the mapping algorithm achieves a 60% reduction in total area compared to a naïve approach.

Single-electron transistor (SET) at room temperature has been demonstrated as a promising device for extending Moore's law due to its ultralow power consumption. However, early realizations of SET array lacked variability and reliability due to their fixed architectures and high defect rates of nanowire segments. Therefore, a reconfigurable version of SET was proposed to deal with these issues. Recently, several automated mapping approaches have been proposed for area minimization of reconfigurable SET arrays. However, to the best of our knowledge, seldom mapping algorithms that consider the existence of defective nanowire segments were proposed. Furthermore, before the defect-aware mapping, we have to know the locations of defects in SET arrays. Thus, this paper presents the first diagnosis approach to identify the locations of defects in SET arrays followed by two defect-aware algorithms for mapping SET arrays in different scenarios. The experimental results show that the proposed diagnosis method can detect 100% of defects under a defect rate and distribution in SET arrays. As for the mapping algorithms, the results show that our approach can successfully map the SET arrays with 11.13% and 7.69% width overhead on average in the baseline detour mapping algorithm and defect-reuse mapping algorithm, respectively, in the presence of 5000-ppm defects.

Steep-slope heterojunction tunnel field-effect transistor (HTFET) devices promise new opportunities beyond CMOS in low-power high-performance communication applications. In this paper, the circuit design optimization of a low-power 14-bit 1-GS/s current-steering digital-to-analog converter (DAC) using 0.4/0.3 V mixed-supply HTFETs is explored. Based on the device characteristics comparison and circuit analysis, it is shown in this paper that HTFET endorses significant differences in both I -V and C -V due to the steep-slope tunneling mechanism and a nature of vertically fabricated structure. While such differences significantly affect the circuit design corners, this paper gives the device-circuit co-optimization for the HTFET DAC, reaching at higher current source output impedance, less nonlinear switching glitch distortions, and thus superior spectral performance over the Si-CMOS DAC. HTFET device variation is also discussed, and calibration techniques are adopted for the static matching accuracy.

Single-Electron Transistor (SET) at room temperature has been demonstrated as a promising device for extending Moore's law due to its ultra low power consumption. However, early realizations of SET array lacked variability and reliability due to their fixed architectures and high defect rates of nanowire segments. Therefore, a reconfigurable version of SET was proposed to deal with these issues. Recently, several automated mapping approaches were proposed for area minimization of reconfigurable SET arrays. However, to the best of our knowledge, no mapping approaches that consider the existence of defective nanowire segments were proposed. Thus, this paper presents the first defect-aware approach for mapping reconfigurable SET arrays. The experimental results show that our approach can successfully map the SET arrays with 20% width overhead on average in the presence of 5000 ppm defects.

Health care continues to be one of the biggest challenges facing our society. Factors such as lifestyle choices, genetics, aging, stress and environmental exposures play a critical role in determining health outcomes. Wearable technologies that can enable continuous/long-term personal health monitoring and personal environmental monitoring can empower users to make better lifestyle decisions and improve health outcomes. While wearable devices promise a compelling future of achieving wellness, current wearable products are not addressing the needs of the health space. To achieve this future, key challenges in wearable systems such as battery life, form factor, sensor functionality, configurability and data analysis will have to be carefully addressed to ensure user adoption and effectively manage health.

We present a novel technique for optimizing the read operation of spin-transfer torque (STT) MRAMs by employing a correlated material in conjunction with a magnetic tunnel junction (MTJ). The design of the proposed memory cell is based on exploiting the orders-of-magnitude difference in the resistance of the two phases of the correlated material (CM) and triggering operation-driven phase transitions in the CM by judiciously co-optimizing devices and the memory cell. During read, the CM operates in the metallic and insulating phases when the MTJ is in the low resistance and high resistance states, respectively. This leads to superior distinguishability, read efficiency and stability. During write, the CM operates in the metallic phase, which minimizes the impact of the CM resistance on the write speed. Our analysis shows that CM amplifies the cell tunneling magneto-resistance from 107% (for the standard STT MRAM) to 1878% (for the proposed cell) leading to 68% higher sense margin. In addition, 45% enhancement in the read disturb margin and 36% reduction in the cell read power is achieved. At the same time, the write asymmetry associated with different state transitions is mildly mitigated, leading to 9% reduction in the write power. This comes at a negligible cost of 4% larger write time. We also discuss the layout implications of our technique and propose the sharing of the CM amongst multiple cells. As a result of the sharing, the proposed technique incurs no area penalty.

Power consumption has become one of the primary challenges to meetMoore's law. For reducing power consumption, single-electron transistor (SET) at room temperature has been demonstrated as a promising device for extending Moore's law due to its ultralow power consumption in operation. Previous works have proposed automated mapping approaches for SET arrays that focused on minimizing the number of hexagons in the SET arrays. However, the area of an SET array is the product of the bounded height and the bounded width, and the height usually equals the number of inputs in the Boolean function. Consequently, in this paper, we focus on the width minimization to reduce the overall area in the mapping of the SET arrays. Our approach consists of techniques of product term minimization, branch-then-share (BTS)-aware variable reordering, SET array architecture relaxation, and BTS-aware product term reordering. The experimental results on a set of MCNC and IWLS 2005 benchmarks show that the proposed approach saves 45% of width compared with the work by Chiang et al., which focused on hexagon count minimization, and also saves 13% of width compared with the work by Chen et al., which focused on width minimization.

Harnessing the computational capabilities of dynamical systems has attracted the attention of scientists and engineers form varied technical disciplines over decades. The time evolution of coupled, non-linear synchronous oscillatory systems has led to active research in understanding their dynamical properties and exploring their applications in brain-inspired, neuromorphic computational models. In this paper we present the realization of coupled and scalable relaxation-oscillators utilizing the metal-insulator-metal transition of vanadium-dioxide (VO2) thin films. We demonstrate the potential use of such a system in pattern recognition, as one possible computational model using such a system.

Power consumption has become one of the primary challenges to meet the Moore's law. For reducing power consumption, Single-Electron Transistor (SET) at room temperature has been demonstrated as a promising device for extending Moore's law due to its ultra-low power consumption during operation. Prior work has proposed an automated mapping approach for SET arrays which focuses on minimizing the number of hexagons in an SET array. However, the area of an SET array is more related to the width. Consequently, in this work, we propose an approach for width minimization of the SET arrays. The experimental results show that the proposed approach saves 26% of width compared with the state-of-the-art for a set of MCNC and IWLS 2005 benchmarks while spending similar CPU time.

The human vision system understands and interprets complex scenes for a variety of visual tasks in real-time while consuming less than 20 Watts of power. The holistic design of artificial vision systems that will approach and eventually exceed the capabilities of human vision systems is a grand challenge. The design of such a system needs advances in multiple disciplines. This paper focuses on advances needed in the computational fabric and provides an overview of a new-genre of architectures inspired by advances in both the understanding of the visual cortex and the emergence of devices with new mechanisms for state computations.

Ultra-low power circuit design techniques have enabled rapid progress in biosignal acquisition. The design of a multi-channel biosignal recording system is a challenging task, considering the low amplitude of neural signals and limited power budget for an implantable system. The front-end low-noise amplifier is a critical component with respect to overall power consumption and noise of such system. In this paper, we present a new design of III-V Heterojunction TFET (HTFET)-based neural amplifier employing a telescopic operational transconductance amplifier (OTA) for multi-channel neural spike recording. Exploiting the unique device characteristics of HTFETs, our simulation shows that the proposed amplifier exhibits a midband gain of 39 dB, a gain bandwidth of 12 Hz-2.1 kHz, and an input-referred noise of 6.27 μVrms, consuming 5 nW of power at a 0.5 V supply voltage. Using the proposed HTFET amplifier, a noise efficiency factor (NEF) of 0.64 is achieved, which is significantly lower than the CMOS-based theoretical limit. Design tradeoffs related to gain, power and noise requirements are investigated, based on a comprehensive electrical noise model of HTFET and compared with the baseline Si FinFET design.

Power consumption has become one of the primary challenges in meeting Moore's law. Fortunately, Single-Electron Transistor (SET) at room temperature has been demonstrated as a promising device for extending Moore's law due to its ultra low power consumption during operation. An automated mapping approach for the SET architecture has been proposed recently for facilitating design realization. In this paper, we propose an enhanced approach consisting of variable reordering, product term reordering, and mapping constraint relaxation techniques to minimizing the area of mapped SET arrays. The experimental results show that our enhanced approach, on average, saves 40% in area and 17% in mapping time compared to the state-of-the-art approach for a set of MCNC and IWLS 2005 benchmarks.

Steep switching Tunnel FETs (TFET) can extend the supply voltage scaling with improved energy efficiency for both digital and analog/RF application. In this paper, recent approaches on III-V Tunnel FET device design, prototype device demonstration, modeling techniques and performance evaluations for digital and analog/RF application are discussed and compared to CMOS technology. The impact of steep switching, uni-directional conduction and negative differential resistance characteristics are explored from circuit design perspective. Circuit-level implementation such as III-V TFET based Adder and SRAM design shows significant improvement on energy efficiency and power reduction below 0.3V for digital application. The analog/RF metric evaluation is presented including gm/Idsmetric, temperature sensitivity, parasitic impact and noise performance. TFETs exhibit promising performance for high frequency, high sensitivity and ultra-low power RF rectifier application.

Hetero-junction Tunnel FET (HTFET) for ultra-low power RF circuit design has been explored at the device and circuit level. In this paper, benchmarking and design insights for optimizing the performance of the TFET based differential drive rectifier is presented. Our evaluation of the HTFET based rectifier demonstrates its promise compared to the state-of-art passive RFIDs. With the 10-stage optimized TFET rectifier at 915 MHz, PCE of 98% with 0.5 nW power consumption, sensitivity of -24dBm for 9 μW PDCand sensitivity of -33dBm for 0.4μW PDCwere achieved.

Reducing power consumption has become one of the primary challenges in chip design, and therefore significant efforts are being devoted to find holistic solutions on power reduction from the device level up to the system level. Among a plethora of low power devices that are being explored, single-electron transistors (SETs) at room temperature are particularly attractive. Although prior work has proposed a binary decision diagram-based reconfigurable logic architecture using SETs, it lacks an automatic synthesis algorithm for the architecture. Consequently, in this work, we develop a product-term-based approach that synthesizes a logic circuit by mapping all its product terms into the SET architecture. The experimental results show the effectiveness and efficiency of the proposed approach on a set of MCNC benchmarks.

Reducing power consumption has become one of the primary challenges in chip design, and therefore significant efforts are being devoted to find holistic solutions on power reduction from the device level up to the system level. Among a plethora of low power devices that are being explored, single-electron transistors (SETs) at room temperature are particularly attractive. Although prior work has proposed a binary decision diagram-based reconfigurable logic architecture using SETs, it lacks an automated synthesis tool for the device. Consequently, in this work, we develop a product-term-based approach that synthesizes a logic circuit by mapping all its product terms into the SET architecture. The experimental results show the effectiveness and efficiency of the proposed approach on a set of MCNC benchmarks.

The steep sub-threshold characteristics of inter-band tunneling FETs (TFETs) make an attractive choice for low voltage operations. In this work, we propose a hybrid TFET-CMOS chip multiprocessor (CMP) that uses CMOS cores for higher voltages and TFETs for lower voltages by exploiting differences in application characteristics. Building from the device characterization to design and simulation of TFET based circuits, our work culminates with a workload evaluation of various single/multi-threaded applications. Our evaluation shows the promise of a new dimension to heterogeneous CMPs to achieve significant energy efficiencies (upto 50% energy benefit and 25% ED benefit with single-threaded applications, and 55% ED benefit with multi-threaded applications).

The emergence of non-volatile memory technologies such as Spin Torque Transfer Magneto-resistive Random Access Memory RAM and Phase Change Memories provide new opportunities for architectural innovations. While the zero off-state leakage, fast read access and high densities of these memories make them attractive options as compared to SRAM, their high write energies and latencies as well as their endurance are a concern. We provide three different architectural techniques that utilize the STT-MRAM characteristics to enable new functionalities. First, we show how exploiting the higher density of STT-MRAM in embedded multi-tasked systems can reduce the context switch overhead. Second, we use the STT-MRAM to create a reliable copy of SRAM structures vulnerable to radiation-induced transient errors to improve reliability. Finally, we show a hybrid cache architecture that uses a mix of emerging TFET technology and STT-MRAM technology. Our results indicate that active leakage is still a concern in STT-MRAM structures.

Steep sub-threshold transistors are promising candidates to replace the traditional MOSFETs for sub-threshold leakage reduction. In this paper, we explore the use of Inter-Band Tunnel Field Effect Transistors (TFETs) in SRAMs at ultra low supply voltages. The uni-directional current conducting TFETs limit the viability of 6 T SRAM cells. To overcome this limitation, 7 T SRAM designs were proposed earlier at the cost of extra silicon area. In this paper, we propose a novel 6 T SRAM design using Si-TFETs for reliable operation with low leakage at ultra low voltages. We also demonstrate that a functional 6 T TFET SRAM design with comparable stability margins and faster performances at low voltages can be realized using proposed design when compared with the 7 T TFET SRAM cell. We achieve a leakage reduction improvement of 700 X and 1600 X over traditional CMOS SRAM designs at VDDof 0.3 V and 0.5 V respectively which makes it suitable for use at ultra-low power applications.

Since 1926 it is well accepted that the continuous nonzero nature of solutions to Schrodinger's wave equation used to represent electrons, even in classically forbidden regions of negative kinetic energy, allows for a finite and tunable probability of tunneling from one classically allowed region to another (for example band to band tunneling in a semiconductor). We have recently initiated the investigation of a novel transistor architecture based on such tunneling mechanism as a step towards exploring steep switching transistors for energy efficient logic applications. In this seminar, I will attempt to address the following topics regarding the tunnel transistor architecture: a) the choice of appropriate materials to tune the transfer characteristics over a specified gate swing b) the characteristic screening lengths to observe saturation in the output characteristics of the device needed to provide gain c) an effective way to estimate the switching speed of such devices and d) the importance (if any) of nonequilibrium carrier dynamics on the device terminal characteristics.

This talk will highlight different generations of energy-efficient device architectures that enable aggressive supply voltage reduction. First, we will discuss the impact of the introduction of a new gate stack with high-K dielectrics and the use of multigate control structures. Next, we will show that incorporation of ultra-high mobility compound (III-V) semiconductors along with corresponding changes in the device channel structure enables supply voltage reduction to below 500mV. Further reduction in the supply voltage to below 250mV range can be achieved through abrupt turn-on exploiting the phenomena of inter-band tunneling in novel vertical transistor architectures. The second part of this talk will focus on new challenges and opportunities in designing circuits and architectures using these emerging device architectures

BACKGROUND: The functions of human cells are carried out by biomolecular networks, which include proteins, genes, and regulatory sites within DNA that encode and control protein expression. Models of biomolecular network structure and dynamics can be inferred from high-throughput measurements of gene and protein expression. We build on our previously developed fuzzy logic method for bridging quantitative and qualitative biological data to address the challenges of noisy, low resolution high-throughput measurements, i.e., from gene expression microarrays. We employ an evolutionary search algorithm to accelerate the search for hypothetical fuzzy biomolecular network models consistent with a biological data set. We also develop a method to estimate the probability of a potential network model fitting a set of data by chance. The resulting metric provides an estimate of both model quality and dataset quality, identifying data that are too noisy to identify meaningful correlations between the measured variables. RESULTS: Optimal parameters for the evolutionary search were identified based on artificial data, and the algorithm showed scalable and consistent performance for as many as 150 variables. The method was tested on previously published human cell cycle gene expression microarray data sets. The evolutionary search method was found to converge to the results of exhaustive search. The randomized evolutionary search was able to converge on a set of similar best-fitting network models on different training data sets after 30 generations running 30 models per generation. Consistent results were found regardless of which of the published data sets were used to train or verify the quantitative predictions of the best-fitting models for cell cycle gene dynamics. CONCLUSION: Our results demonstrate the capability of scalable evolutionary search for fuzzy network models to address the problem of inferring models based on complex, noisy biomolecular data sets. This approach yields multiple alternative models that are consistent with the data, yielding a constrained set of hypotheses that can be used to optimally design subsequent experiments.