This paper presents an all open-source framework for adding embedded FPGAs into RISC-V CPUs. In our approach, an eFPGA is directly coupled with the CPU, and through supporting partial reconfiguration, instructions can be swapped at runtime. The eFPGA fabric is tiled into multiple slots in order to host different instructions in parallel, and multiple slots can be combined for hosting more complex instructions. Instructions can be swapped without interrupting the CPU, and instructions can have a different number of execution cycles to provide more flexibility for instruction implementations. Our case study integrates an Ibex RISC-V core from lowRISC together with our custom embedded FPGA supporting multiple regions, with logic, DSP, and Register File slices. This system had been taped out in a 180um TSMC process.

There is an increasing interest for alternative ways to program memristive devices to arbitrary resistive levels. Among them, light-controlled programming approach, where optical input is used to improve or to promote the resistive switching, has drawn particular attention. Here, we present a straight-forward method to induce resistive switching to a memristive device, introducing a new version of a metal-oxide memristive architecture coupled with a UV-sensitive hybrid top electrode obtained through direct surface treatment with PEDOT:PSS of an established resistive random access memory platform. UV-illumination ultimately results to resistive switching, without involving any additional stimulation, and a relation between the switching magnitude and the applied wavelength is depicted. Overall, the system and method presented showcase a promising proof-of-concept for granting an exclusively light-triggered resistive switching to memristive devices irrespectively of the structure and materials comprising their main core, and, in perspective can be considered for functional integrations optical-induced sensing.

There is an increasing interest for alternative ways to program memristive devices to arbitrary resistive levels. Among them, light-controlled programming approach, where optical input is used to improve or to promote the resistive switching, has drawn particular attention. Here, we present a straight-forward method to induce resistive switching to a memristive device, introducing a new version of a metal-oxide memristive architecture coupled with a UV-sensitive hybrid top electrode obtained through direct surface treatment with PEDOT:PSS of an established resistive random access memory platform. UV-illumination ultimately results to resistive switching, without involving any additional stimulation, and a relation between the switching magnitude and the applied wavelength is depicted. Overall, the system and method presented showcase a promising proof-of-concept for granting an exclusively light-triggered resistive switching to memristive devices irrespectively of the structure and materials comprising their main core, and, in perspective can be considered for functional integrations optical-induced sensing.

Volatility in metal-oxide resistive random access memory (RRAM) families has mostly been treated as an unwanted side-effect, although recently there are trends to interpret such behavior as an additional technological feature. To date, the field has seen early demonstrations of possible applications that harness volatility. Moreover, some work has been conducted to understand both the mechanisms responsible for this behavior. In the context of modeling RRAM volatility, we still lack a comprehensive model that could allow simulations in a larger scale. In an attempt to fill this gap, this work presents a modeling framework that can account for RRAM relaxation characteristics. Specifically, we show how volatility can be simulated to significant accuracy when the resistive state (RS) of a device as well as the stimulus protocol in use are well-defined. Importantly, our approach is solely data-driven and decoupled from previous physical modeling studies on volatility. Our results work for both stimulation polarities and are consistent for a number of TiO x devices in use. Moreover, the mathematical relations that unfold via modeling volatility provide further intuition on the effect that invasive protocols can have on this technology. This modeling solution enables more advanced studying of memristive technologies in one hand, as well as more intricate designs of larger systems that can account for transient RRAM changes over time.

Neuromorphic systems provide an alternative to conventional computing hardware, promising low-power operation suitable for sensory-processing and edge computing. In this paper, we present a mixed-signal processing system designed to provide on-sensor classification of signals obtained from multielectrode array neural recordings. The designed circuits implement a real-time spike sorting algorithm, and operate on signals represented by asynchronous event streams. We combine analog circuits computation primitives (temporal surface generation, distance computation, winner-take-all) to implement a spatiotemporal clustering algorithm, classifying signals acquired by
neighbouring electrodes. The prototype chip has been submitted for fabrication in a 180nm CMOS technology. The circuits are designed to fit, alongside signal conditioning and conversion circuits, in the area under the recording electrodes (below 80x80um per electrode). Circuit implementation details and simulation results are presented. The expected neural spike recognition rates of 75% in a single-layer network and 88% in a 2-layer network are comparable with a software implementation, while the system is designed to provide a low-power embedded real-time solution.
This work provides a foundation towards the design of a large scale neuromorphic processing system, to be embedded in brainmachine interfaces.

A methodology is introduced here to exploit the programmability of the memristors in order to realize reconfigurable monolithic analogue circuit elements. Classical network synthesis methods are used to synthesize adjustable active inductors with inductance values exceeding those of on-chip passives by several orders of magnitude. In this paper, a wide range of active inductance values are obtained by employing memristor to control the biasing current of operational transconductance amplifiers used to implement gyrators. The gyration constant of the proposed gyrator will be linearly controlled by memristance state. The implementation of the designed circuit is realized in 0.18μm commercially available complementary metal-oxide-semiconductor (CMOS) technology from TSMC. Circuit performance is simulated using Cadence Virtuoso. The utilized off-chip memristor is a metal-oxide bi-layer memristor which exhibits a non-volatile memristance range of 4.7kΩ to 170kΩ. The active inductance range achieved is from approximately 95μH to 1.55mH with an inductive bandwidth of 69MHz and 18MHz respectively. The total power consumption is between 0.21mW to 1.95mW depending on the memristance and equivalent inductance.

Memristor crossbar arrays offer a novel new approach for designing high density non-volatile memory; however, precise measurement of resistive crossbar elements requires parallel current sensing capability not found in existing instruments. To provide this capability, we have designed and built an FPGA-based crossbar control instrument with independent per-channel biasing and measuring. In this paper, we cover the architecture of this new instrument, its operation and interface, and the results of testing conducted on the instruments pulse driver circuitry.

In this demonstration, we present a new electro-forming process, along with a new instrument to support this procedure. Memristor arrays will be available for the user to electroform, write, and read the resulting resistive state of the devices.

Medical interventions increasingly rely on biosensors that can provide reliable quantitative information. A longstanding bottleneck in realizing this, is various non-idealities that generate offsets and variable responses across sensors. Current mitigation strategies involve the calibration of sensors, performed in software or via auxiliary compensation circuitry thus constraining real-time operation and integration efforts. Here, we show that bio-functionalized metal-oxide memristors can be utilized for directly transducing biomarker concentration levels to discrete memory states. The introduced chemical state-variable is found to be dependent on
the devices’ initial resistance, with its response to chemical stimuli being more pronounced for higher resistive states. We leverage this attribute along with memristors’ inherent state programmability for calibrating a biosensing array to render a homogeneous response across all cells. Finally, we demonstrate the application of this technology in detecting Prostate Specific Antigen in clinically relevant levels (ng/ml), paving the way towards applications in large multi-panel assays.

The multistate capabilities as well as the intrinsic integrating properties of memristors deem them suitable candidates for the realization of novel neuromorphic applications. To date, much of their prestige arises mostly from the versatility that is promised by the nonvolatile device families. However, memristors also exhibit volatile characteristics, which for as long as they remain unknown, will hinder their integration to large-scale applications. In this article, we present a comprehensive study for characterizing the relaxation dynamics of TiOₓ resistive RAM (RRAM) devices within a predefined volatility framework. These dynamics are tightly linked to the total energy of stimulation, and device relaxation can be accurately described using simple mathematical models. Moreover, we show that RRAM volatility is bidirectional and that relaxation time constants heavily depend on the level of invasiveness caused by programming stimulation. Our work further includes a demonstration of how volatility can be characterized within a specific time window. Moreover, our protocol can be altered to fit the specific needs of potential applications. We anticipate that the universality of our method can act as a stepping stone toward the understanding and modeling of volatile memristors across different technologies and materials, enabling the realization of a new family of time-related applications.

Oxidation of TiN is a diffusion-limited process due to the high stability of the TiN metallic state at the TiN/TiO2 junction. Hence, the TiN/TiO2/TiN device being the inability to form a suitable interfacial layer results in the exhibition of abrupt current (conductance) rise and fall during the set (potentiation) and reset (depression) processes, respectively. Interfacial engineering by depositing Ti film served as the oxygen gettering material on top of the TiO2 layer induces a spontaneous reaction to form a TiOx interfacial layer (due to the low Gibbs free energy of suboxide formation). Such an interface layer acts as an oxygen reservoir that promotes gradual oxidation and reduction during the set and reset processes. Consequently, an excellent analog behavior having a 2-bit per cell and robust epoch training can be achieved. However, a thick interfacial layer may degrade the switching behavior of the device due to the high internal resistance. This work suggests that interfacial engineering could be considered in designing high-performance analog memristor devices.

Memristor devices that can operate at high speed with high density and non-volatile capabilities have great potential for the development of high data storage and robust wearable devices. However, in real-time the performance of memristors are challenged by their instability towards harsh working conditions such as high temperature, extreme humidity, photo irradiation and mechanical bending. Herein, we introduce a TaOx/AlN based flexible and transparent memristor device having stable endurance under extreme 2 mm bending (for more than 107 cycles) with ON/OFF ratio of more than 2 orders of magnitude at 25 ns rapid switching. This device performs excellent flexibility under extreme bending conditions (bending radius of 2 mm) even with intense ultraviolet radiation. A thin AlN insertion layer having low dielectric and high thermal conductivity play a crucial role in improving the switching stability and device flexibility. In particular, the devices exhibit excellent minimum switching fluctuations under UV irradiations, 105 s nonvolatility retention at high temperature (135˚C), various gas ambient and, damp heat test (humidity 95.5%, 83˚C) due to the indium metal drift during switching process and high bonding energy of Ta-O. Most importantly, direct observation of indium metal strongly anchored in TaOx switching layer during switching process is reported for the first time via transmission electron microscopy which provides clear insights on the switching phenomenon. Furthermore, results of electrical and material analyses explain that our facile device design has excellent potential for wearable and aerospace applications.

Memristor devices that can operate at high speed with high density and nonvolatile capabilities have great potential for the development of high data storage and robust wearable devices. However, in real-time, the performance of memristors is challenged by their instability toward harsh working conditions such as high temperature, extreme humidity, photo irradiation, and mechanical bending. Herein, we introduce a TaOx/AlN-based flexible and transparent memristor device having stable endurance under extreme 2 mm bending (for more than 107 cycles) with an ON/OFF ratio of more than 2 orders of magnitude at 25 ns rapid switching. This device exhibits excellent flexibility under extreme bending conditions (bending radius of 2 mm) even with intense ultraviolet (UV) radiation. A thin AlN insertion layer having low dielectric and high thermal conductivity plays a crucial role in improving the switching stability and device flexibility. In particular, the devices exhibit excellent minimum switching fluctuations under UV irradiation, >106 s nonvolatility retention at high temperature (135 °C), various gas ambient, and damp heat test (humidity 95.5%, 83 °C) because of the indium metal drift during the switching process and high bonding energy of Ta–O. Most importantly, direct observation of indium metal strongly anchored in the TaOx switching layer during the switching process is reported for the first time via transmission electron microscopy, which provides clear insights into the switching phenomenon. Furthermore, the results of electrical and material analyses explain that our facile device design has excellent potential for wearable and aerospace applications.

The holy grail of analogue integrated circuit design is adjustable analogue delay element. Of course, all analogue circuits are filters. Internal delays impose overall low-pass character to all circuits so that broadband amplifiers are lowpass filters, while high-pass amplifiers are in fact band-pass filters.

The emergence of memristor technologies has recently received much attention due to their promising features, expecting to be a key driver in the post-CMOS era. With its ultra-low power, higher density capability and non-volatile characteristics, memristor technology is considered as the best candidate to replace SRAM cells or be employed for routing in digital reconfigurable systems. Although memristor-based reconfigurable circuits can offer many advantages over the conventional CMOS designs, limitations in the utilization of memristor technologies such as electroforming or programming structures have not been thoroughly considered and discussed. This work looks into recent trends in exploiting memristor technologies in reconfigurable circuits and then discusses implementation challenges like memristor programming, reliability and operation of memristor-based memory cells for digitally reconfigurable circuits.

The holy grail of analogue integrated circuit design is adjustable analogue delay element. Of course, all analogue circuits are filters. Internal delays impose overall low-pass character to all circuits so that broadband amplifiers are lowpass filters, while high-pass amplifiers are in fact band-pass filters.

Brain function relies on circuits of spiking neurons with synapses playing the key role of merging transmission with memory storage and processing. Electronics has made important advances to emulate neurons and synapses and brain-computer interfacing concepts that interlink brain and brain-inspired devices are beginning to materialise. We report on memristive links between brain and silicon spiking neurons that emulate transmission and plasticity properties of real synapses. A memristor paired with a metal-thin film titanium oxide microelectrode connects a silicon neuron to a neuron of the rat hippocampus. Memristive plasticity accounts for modulation of connection strength, while transmission is mediated by weighted stimuli through the thin film oxide leading to responses that resemble excitatory postsynaptic potentials. The reverse brain-to-silicon link is established through a microelectrode-memristor pair. On these bases, we demonstrate a three-neuron brain-silicon network where memristive synapses undergo long-term potentiation or depression driven by neuronal firing rates.