Artificial Intelligence (AI) at the edge has become a hot subject of the recent technology-minded publications. The challenges related to IoT nodes gave rise to research on efficient hardware-based accelerators. In this context, analog memristor devices are crucial elements to efficiently perform the multiply-and-add (MAD) operations found in many AI algorithms. This is due to the ability of memristor devices to perform in-memory-computing (IMC) in a way that mimics the synapses in human brain. Here, we present a novel planar analog memristor, namely NeuroMem, that includes a partially reduced Graphene Oxide (prGO) thin film. The analog and non-volatile resistance switching of NeuroMem enable tuning it to any value within the RON and ROFF range. These two features make NeuroMem a potential candidate for emerging IMC applications such as inference engine for AI systems. Moreover, the prGO thin film of the memristor is patterned on a flexible substrate of Cyclic Olefin Copolymer (COC) using standard microfabrication techniques. This provides new opportunities for simple, flexible, and cost-effective fabrication of solution-based Graphene-based memristors. In addition to providing detailed electrical characterization of the device, a crossbar of the technology has been fabricated to demonstrate its ability to implement IMC for MAD operations targeting fully connected layer of Artificial Neural Network. This work is the first to report on the great potential of this technology for AI inference application especially for edge devices.

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In this paper, we present a novel Pt/CuO/Pt metal-oxide-metal (MOM) glucose sensor. The devices are fabricated using a simple, low-cost standard photolithography process. The unique planar structure of the device provides a large electrochemically active surface area, which acts as a nonenzymatic reservoir for glucose oxidation. The sensor has a linear sensing range between 2.2 mM and 10 mM of glucose concentration, which covers the blood glucose levels for an adult human. The distinguishing property of this sensor is its ability to measure glucose at neutral pH conditions (i.e. pH = 7). Furthermore, the dilution step commonly needed for CuO-based nonenzymatic electrochemical sensors to achieve an alkaline medium, which is essential to perform redox reactions in the absence of glucose oxidase, is eliminated, resulting in a lower-cost and more compact device.

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In this paper, the memristive switching behavior of Cu/ HfO 2 /p ++ Si devices fabricated by an organic-polymer-assisted sol-gel spin-coating method, coupled with post-annealing and shadow-mask metal sputtering steps, is examined. HfO 2 layers of about 190 nm and 80 nm, are established using cost-effective spin-coating method, at deposition speeds of 2000 and 4000 rotations per minute (RPM), respectively. For two types of devices, the memristive characteristics ( V on , I on , and V reset ) and device-to-device electrical repeatability are primarily discussed in correlation with the oxide layer uniformity and thickness. The devices presented in this work exhibit an electroforming free and bipolar memory-resistive switching behavior that is typical of an Electrochemical Metallization (ECM) I-V fingerprint. The sample devices deposited at 4000 RPM generally show less variation in electrical performance parameters compared to those prepared at halved spin-coating speed. Typically, the samples prepared at 4000 RPM (n = 8) display a mean switching voltage V on of 3.0 V (±0.3) and mean reset voltage V reset of −1.1 V (±0.5) over 50 consecutive sweep cycles. These devices exhibit a large R off / R on window (up to 10 4 ), and sufficient electrical endurance and retention properties to be further examined for radiation sensing. As they exhibit less statistical uncertainty compared to the samples fabricated at 2000 RPM, the devices prepared at 4000 RPM are tested for the detection of soft gamma rays (emitted from low-activity Cs-137 and Am-241 radioactive sources), by assessing the variation in the on-state resistance value upon exposure. The analysis of the probability distributions of the logarithmic R on values measured over repeated ON-OFF cycles, before, during and after exposing the devices to radiation, demonstrate a statistical difference. These results pave the way for the fabrication and development of cost-effective soft-gamma ray detectors.

Recently, graphene has been explored in several research areas according to its outstanding combination of mechanical and electrical features. The ability to fabricate micro-patterns of graphene facilitates its integration in emerging technologies such as flexible electronics. This work reports a novel micro-pattern approach of graphene oxide (GO) film on a polymer substrate using metal bonding. It is shown that adding ethanol to the GO aqueous dispersion enhances substantially the uniformity of GO thin film deposition, which is a great asset for mass production. On the other hand, the presence of ethanol in the GO solution hinders the fabrication of patterned GO films using the standard lift-off process. To overcome this, the fabrication process provided in this work takes advantage of the chemical adhesion between the GO or reduced GO (rGO) and metal films. It is proved that the adhesion between the metal layer and GO or rGO is stronger than the adhesion between the latter and the polymer substrate (i.e., cyclic olefin copolymer used in this work). This causes the removal of the GO layer underneath the metal film during the lift-off process, leaving behind the desired GO or rGO micro-patterns. The feasibility and suitability of the proposed pattern technique is confirmed by fabricating the patterned electrodes inside a microfluidic device to manipulate living cells using dielectrophoresis. This work adds great value to micro-pattern GO and rGO thin films and has immense potential to achieve high yield production in emerging applications.