The linear conductance update of a CMOS-compatible HfO₂ memristor is improved by introducing a second-order memristor effect and connecting a voltage divider to the device, which makes the memristor crossbar array more energy- and time-efficient.

Haejun Lee, Akhil Kedia, Jongwon Lee, Ashwin Paranjape, Christopher Manning, Kyoung-Gu Woo. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022.

Edge computing devices, which generate, collect, process, and analyze data near the source, enhance the data processing efficiency and improve the responsiveness in real-time applications or unstable network environments. To be utilized in wearable and skin-attached electronics, these edge devices must be compact, energy efficient for use in low-power environments, and fabricable on soft substrates. Here, we propose a flexible memristive dot product engine (f-MDPE) designed for edge use and demonstrate its feasibility in a real-time electrocardiogram (ECG) monitoring system. The f-MDPE comprises a 32 × 32 crossbar array embodying a low-temperature processed self-rectifying charge trap memristor on a flexible polyimide substrate and exhibits high uniformity and robust electrical and mechanical stability even under 5-mm bending conditions. Then, we design a neural network training algorithm through hardware-aware approaches and conduct real-time edge ECG diagnosis. This approach achieved an ECG classification accuracy of 93.5%, while consuming only 0.3% of the energy compared to digital approaches, highlighting the strong potential of this approach for emerging edge neuromorphic hardware.

The sensory nervous system in animals enables the perception of external stimuli. Developing an artificial sensory nervous system has been widely conducted to realize neuro-inspired robots capable of effectively responding to external stimuli. However, it remains challenging to develop artificial sensory nervous systems that possess sophisticated biological functions, such as habituation and sensitization, enabling efficient responses without bulky peripheral circuitry. Here, we introduce a memristor device with third-order switching complexity, emulating an artificial synapse that inherently possesses habituation and sensitization properties. Incorporating an additional resistive switching TiO_x layer into the HfO₂ memristor exhibits third-order switching complexity and non-volatile habituation characteristics. Based on the third-order memristor, we propose a robotic system equipped with a memristor-based artificial sensory nervous system for optimizing the robot arm’s response to external stimuli without the aid of processors. It is experimentally demonstrated that the robot arm with the developed memristor-based artificial sensory nervous system ignores approximately 71% of safe and familiar stimuli while sensitively responding to threatening and significant stimuli, similar to the habituation and sensitization of biological sensory nervous systems. Our findings can be a stepping stone for energy-efficient and intelligent robotic systems with reduced hardware burden.

A high-efficient terahertz (THz) oscillator based on an InP-based resonant tunneling diode (RTD) is proposed. The oscillator is designed by utilizing a low-frequency bias stabilizer consisting of a resistor and a capacitor connected in series, along with utilizing a negative differential resistance (NDR) oscillator topology. The oscillator is fabricated by using an InP monolithic THz integrated circuit multi-layer process. The fabricated oscillator shows a high dc-to-rf power efficiency of 0.274 % with a low dc power consumption of 3.4 mW and an RF output power of 9.3 W at an oscillation frequency of 692 GHz. The achieved high efficiency is attributed to the use of the bias stabilizer, along with low-power NDR operation of the RTD.

Many electrochemical biosensors operate based on a threshold-sensing (TS) method, which indicates the presence of a disease when the concentration of a biomarker exceeds a predetermined, disease-specific threshold. The TS in biosensor systems is often implemented using power-hungry signal processing (SP) modules or computers, which increases energy consumption and system complexity. Here, we propose a memristor-based bio-to-electrical transducer with built-in TS functionality, allowing TS to be performed directly within the transducer instead of relying on SP modules and computers. Fabricated resistive random-access memory-based TaO _X /Ta ₂ O ₅ memristors meet the transducer requirements, such as a high on/off ratio greater than 30 while maintaining a long unit pulse width exceeding 10 μs. The intended operation of the proposed transducer was experimentally confirmed by the immediate change in resistance of the memristor (R _M ) from high resistance state to low resistance state. Using this proposed transducer, a complete electrochemical biosensor system was implemented by integrating a sensor electrode for pH sensing, an SP module, and a display with the proposed transducer. The memristor-based system offers flexible control of the threshold pH point through a simple design, making it well-suited for point-of-care diagnostics, where portability is highly essential.