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Emerging device technologies such as resistive RAM (RRAM) are increasingly recognized in enhancing system performance, particularly in applications demanding extensive vector-matrix multiplications (VMMs) with high parallelism. However, a significant limitation in current electronics design automation (EDA) tools is their lack of support for rapid prototyping, design space exploration, and the integration of inherent process-dependent device variability into system-level simulations, which is essential for assessing system reliability. To address this gap, we introduce a field-programmable gate array (FPGA) based emulation approach for RRAM crossbars featuring cycle-accurate emulations in real time without relying on complex device models. Our approach is based on pre-generated look-up tables (LUTs) to accurately represent the RRAM device behavior. To efficiently model the device variability at the system level, we propose using the multivariate kernel density estimation (KDE) method to augment the measured RRAM data. The proposed emulator allows precise latency determination for matrix mapping and computation operations. Meanwhile, by coupling with the NeuroSim framework, the corresponding energy consumption can be estimated. In addition to facilitating a range of in-depth system assessments, experimental results suggest a remarkable reduction of emulation time compared to the classic behavioral simulation.
AbstractBlooming and pruning is one of the most important developmental mechanisms of the biological brain in the first years of life, enabling it to adapt its network structure to the demands of the environment. The mechanism is thought to be fundamental for the development of cognitive skills. Inspired by this, Chialvo and Bak proposed in 1999 a learning scheme that learns from mistakes by eliminating from the initial surplus of synaptic connections those that lead to an undesirable outcome. Here, this idea is implemented in a neuromorphic circuit scheme using CMOS integrated HfO2-based memristive devices. The implemented two-layer neural network learns in a self-organized manner without positive reinforcement and exploits the inherent variability of the memristive devices. This approach provides hardware, local, and energy-efficient learning. A combined experimental and simulation-based parameter study is presented to find the relevant system and device parameters leading to a compact and robust memristive neuromorphic circuit that can handle association tasks.
This paper presents a read circuit design for resistive memory cells based on current mirrors. The circuit utilizes high-precision current mirrors and reference cells to determine the state of resistive memory using comparators. It offers a high degree in adaptability in terms of both resistance range and number of levels. Special emphasis was put on device protection to prevent accidental programming of the memory during read operations. The realized circuit can resolve eight states with a resolution of up to 1 k Ω, realizing a digitization of the analog memory information. Furthermore, the integration in a complete memory macro is shown. The circuit was realized in a 130 nm-process but can easily be adapted to other processes and resistive memory technologies.
High Gain Graphene Based Hot Electron Transistor with Record High Saturated Output Current Density
(2024)
Hot electron transistors (HETs) represent an exciting frontier in semiconductor technology, holding the promise of high-speed and high-frequency electronics. With the exploration of two-dimensional materials such as graphene and new device architectures, HETs are poised to revolutionize the landscape of modern electronics. This study highlights a novel HET structure with a record output current density of 800 A/cm² and a high current gain α, fabricated using a scalable fabrication approach. The HET structure comprises two-dimensional hexagonal boron nitride (hBN) and graphene layers wet transferred to a germanium substrate. The combination of these materials results in exceptional performance, particularly in terms of the highly saturated output current density. The scalable fabrication scheme used to produce the HET opens up opportunities for large-scale manufacturing. This breakthrough in HET technology holds promise for advanced electronic applications, offering high current capabilities in a practical and manufacturable device.
The combination of two-dimensional materials, such as graphene, with established thin films offers great opportunities for enabling next-generation vertical transistors for various applications. This paper gives a brief overview about different vertical transistor concepts using twodimensional materials proposed so far, e.g. the hot electron transistor and the Barristor. With the arrival of twodimensional materials, the hot electron transistor also experienced a revival with predicted cut-off frequencies in the THz range. The Barristor overcomes the weak current saturation of lateral graphene field-effect transistors and high on-off ratios up to 107 were demonstrated, which are suitable parameters for logic applications. By combining a
semiconductor-graphene-semiconductor design of the simplest hot electron transistor with the Barristor operating principle a new device, called graphene adjustable-barriers transistor, can be realized. This new device concept provides the potential for RF, power electronics, and optoelectronic applications.
Due to the unique electronic band structure, graphene has opened great potential to extend the functionality of a large variety of devices. Despite the significant progress in the fabrication of various graphene based microelectronic devices, the integration of graphene devices still lack the stability and compatibility with Si-technology processes. Therefore, the investigation and preparation of graphene devices in conditions resembling as close as possible the Si technology environment is of highest importance. This study aims to explore various aspects of graphene research on a 200mm pilot line, with a focus on simulations and fabrication of graphene modulator. To be more precise, it includes design and fabrication of the layouts, necessary mask sets, creation of the flows, fabrication, and measurements of the Gr modulators on 200 mm wafers.
Study on the metal -graphene contact resistance achieved with one -dimensional contact architecture
(2023)
Graphene has always been considered as one of the materials with the greatest potential for the realization of improved microelectronic and photonic devices. But to actually reach its full potential in Si CMOS technology, graphene
-based devices need to overcome different challenges. They do not only
need to have better performances than standard devices, but they also need to be compatible with the production of standard Si based devices. To address the first challenge the main route requires the optimization of the contact resistance, that highly reduces the devices performance, while the second challenges requires the integration of graphene inside the standard production lines used for microelectronic. In this work we used an 8” wafer pilot -line to realize our devices and we studied the behavior of the contact resistance between metal and graphene obtained by one -dimensional contact architecture between the two materials. The contact resistance has been measured by means of Transmission Line Method (TLM) with several contact patterning.
Memristive devices integrated in 200 mm wafers
manufactured in 130 nm CMOS technology with two different
dielectrics, namely, a HfO2 monolayer and an Al2O3/HfO2 bilayer,
have been measured. The cycle-to-cycle (C2C) and device-todevice (D2D) variability have been analyzed at the wafer scale using different numerical methods to extract the set (Vset) and reset (Vreset) voltages. Some interesting differences between both technologies were found in terms of switching characteristics
This work presents a quasi-static electrical characterization of 1-transistor-1-resistor memristive structures designed following hardness-by-design techniques integrated in the CMOS fabrication process to assure multi-level capabilities in harsh radiation environments. Modulating the gate voltage of the enclosed layout transistor connected in series with the memristive device, it was possible to achieve excellent switching capabilities from a single high resistance state to a total of eight different low resistance states (more than 3 bits). Thus, the fabricated devices are suitable for their integration in larger in-memory computing systems and in multi-level memory applications.
Index Terms—radiation-hard, hardness-by-design, memristive
devices, Enclosed Layout Transistor, in-memory computing
In-memory computing with resistive-switching random access memory (RRAM) crossbar arrays has the potential to overcome the major bottlenecks faced by digital hardware for data-heavy workloads such as deep learning. However, RRAM devices are subject to several non-idealities that result in significant inference accuracy drops compared with software baseline accuracy. A critical one is related to the drift of the conductance states appearing immediately at the end of program and verify algorithms that are mandatory for accurate
multi-level conductance operation. The support of drift models
in state-of-the-art simulation tools of memristive computationin-memory is currently only in the early stage, since they
overlook key device- and array-level parameters affecting drift
resilience such as the programming algorithm of RRAM cells,
the choice of target conductance states and the weight-toconductance mapping scheme. The goal of this paper is to fully expose these parameters to RRAM crossbar designers as a multi-dimensional optimization space of drift resilience. For this purpose, a simulation framework is developed, which comes with the suitable abstractions to propagate the effects of those RRAM crossbar configuration parameters to their ultimate implications over inference performance stability.
We characterize TiN/Ti/HfO2/TiN memristive devices for neuromorphic computing. We analyze different features that allow the devices to mimic biological synapses and present the models to reproduce analytically some of the data measured. In particular, we have measured the spike timing dependent plasticity behavior in our devices and later on we have modeled it. The spike timing dependent plasticity model was implemented as the learning rule of a spiking neural network that was trained to recognize the MNIST dataset. Variability is implemented and its influence on the network recognition accuracy is considered accounting for the number of neurons in the network and the number of training epochs. Finally, stochastic resonance is studied as another synaptic feature.It is shown that this effect is important and greatly depends on the noise statistical characteristics.
The use of resistive random-access memory (RRAM) for in-memory computing (IMC) architectures has significantly improved the energy-efficiency of artificial neural networks (ANN) over the past years. Current RRAM-technologies are physically limited to a defined unambiguously distinguishable number of stable states and a maximum resistive value and are compatible with present complementary metal-oxide semiconductor (CMOS)-technologies. In this work, we improved the accuracy of current ANN models by using increased weight resolutions of memristive devices, combining two or more in-series RRAM cells, integrated in the back end of line (BEOL) of the CMOS process. Based on system level simulations, 1T2R devices were fabricated in IHP's 130nm SiGe:BiCMOS technology node, demonstrating an increased number of states. We achieved an increase in weight resolution from 3 bit in ITIR cells to 6.5 bit in our 1T2R cell. The experimental data of 1T2R devices gives indications for the performance and energy-efficiency improvement in ITNR arrays for ANN applications.
Resistive Random Access Memory (RRAM) technology holds promises to improve the Field Programmable Gate Array (FPGA) performance, reduce the area footprint, and dramatically lower run-time energy requirements compared to the state-of-the-art CMOS-based products. However, the integration of RRAM in FPGAs is hindered by the high programming power consumption and by non-ideal behaviors of the device due to its stochastic nature that may overshadow the benefits in normal operation mode. To cope with these challenges, optimized programming strategies have to be investigated. In this work, we explore the impact that different procedures to set the device have on the run-time performance. Process, voltage, and temperature (PVT) variations as well as time-dependent drift effect of the RRAM device are considered in the assessment of 4T1R MUX designs characteristics. The comparison with tradition CMOS implementations reveals how the choice of the target resistive state and the programming algorithm are key design aspects to reduce the run-time delay and energy metrics, while at the same time improving the robustness against the different sources of variations.
In this paper, we present a method for highly-efficient circuit simulation of a hardware-based artificial neural network realized in a memristive crossbar array. The statistical variability of the devices is considered by a noise-based simulation technique. For the simulation of a crossbar array with 8 synaptic weights in Cadence Virtuoso the new approach shows a more than 200x speed improvement compared to a Monte Carlo approach, yielding the same results. In addition, first results of an ANN with more than 15,000 memristive devices classifying test data of the MNIST dataset are shown, for which the speed improvement is expected to be several orders of magnitude. Furthermore, the influence on the classification of parasitic resistances of the connection lines in the crossbar is shown.
For the fabrication of modern graphene devices, uniform growth of high-quality monolayer graphene on wafer scale is important. This work reports on the growth of large-scale graphene on semiconducting 8 inch Ge(110)/Si wafers by chemical vapor deposition and a DFT analysis of the growth process. Good graphene quality is indicated by the small FWHM (32 cm–1) of the Raman 2D band, low intensity ratio of the Raman D and G bands (0.06), and homogeneous SEM images and is confirmed by Hall measurements: high mobility (2700 cm2/Vs) and low sheet resistance (800 Ω/sq). In contrast to Ge(001), Ge(110) does not undergo faceting during the growth. We argue that Ge(001) roughens as a result of vacancy accumulation at pinned steps, easy motion of bonded graphene edges across (107) facets, and low energy cost to expand Ge area by surface vicinals, but on Ge(110), these mechanisms do not work due to different surface geometries and complex reconstruction.