Approaches for Memristive Structures Using Scratching Probe Nanolithography: Towards Neuromorphic Applications

This paper proposes two different approaches to studying resistive switching of oxide thin films using scratching probe nanolithography of atomic force microscopy (AFM). These approaches allow us to assess the effects of memristor size and top-contact thickness on resistive switching. For that purpose, we investigated scratching probe nanolithography regimes using the Taguchi method, which is known as a reliable method for improving the reliability of the result. The AFM parameters, including normal load, scratch distance, probe speed, and probe direction, are optimized on the photoresist thin film by the Taguchi method. As a result, the pinholes with diameter ranged from 25.4 ± 2.2 nm to 85.1 ± 6.3 nm, and the groove array with a depth of 40.5 ± 3.7 nm and a roughness at the bottom of less than a few nanometers was formed. Then, based on the Si/TiN/ZnO/photoresist structures, we fabricated and investigated memristors with different spot sizes and TiN top contact thickness. As a result, the HRS/LRS ratio, U_SET, and I_LRS are well controlled for a memristor size from 27 nm to 83 nm and ranged from ~8 to ~128, from 1.4 ± 0.1 V to 1.8 ± 0.2 V, and from (1.7 ± 0.2) × 10^-10 A to (4.2 ± 0.6) × 10^-9 A, respectively. Furthermore, the HRS/LRS ratio and U_SET are well controlled at a TiN top contact thickness from 8.3 ± 1.1 nm to 32.4 ± 4.2 nm and ranged from ~22 to ~188 and from 1.15 ± 0.05 V to 1.62 ± 0.06 V, respectively. The results can be used in the engineering and manufacturing of memristive structures for neuromorphic applications of brain-inspired artificial intelligence systems.

A Memristive Circuit Implementation of Eyes State Detection in Fatigue Driving Based on Biological Long Short-Term Memory Rule

Biological long short-term memory (B-LSTM) can effectively help human process all kinds of received information. In this work, a memristive B-LSTM circuit which mimics a conversion from short-term memory to long-term memory is proposed. That is, the stronger the signal, the more profound the memory and the higher the output. On this basis, an image binarization circuit using adaptive row threshold algorithm is proposed. It can make the image remain a deep impression on the strong pixel information and effectively filter the relatively weak pixel information. In combination with the function of image binarization, a memristive circuit for eyes state detection is proposed by adding corresponding horizontal projection calculation, subtraction calculation and judgement open or closed eyes modules. The proposed circuit can detect whether there is a blink between two adjacent facial images, which uses the characteristics of memristor to detect the difference of horizontal projection between two images. Due to the use of memristor, the proposed circuit can realize in-memory computing, which fundamentally avoids the problem of storage wall and shorten the execution time. Finally, an expectation application in fatigue driving based on the proposed method is demonstrated, which indicates the practicability of the circuit design in this work.

The Design of Memristive Circuit for Affective Multi-Associative Learning

In this work, a memristive circuit with affective multi-associative learning function is proposed, which mimics the process of human affective formation. It mainly contains three modules: affective associative learning, affective formation, affective expression. The first module is composed of several affective single-associative learning circuits consisting of memristive neurons and synapses. Memristive neuron will be activated and output pulses if its input exceeds the threshold. After it is activated, memristive neuron can automatically return to the inactive state. Memristive synapse can realize learning and forgetting functions based on the signals from pre- and post-neurons. The learning rule is pre-neuron activated lags behind post-neuron for a short time; the forgetting rule is to repeatedly activate pre-neuron after the emotion is learned. The process of learning or forgetting corresponds to facilitating or inhibiting synaptic weight, that is, decreasing or increasing memristance continuously. Different voltage signals applied to memristors and different parameters of memristors would lead to different synaptic weights which indicate different affective association. The second module can convert affective signals to corresponding emotions. The formed emotions can be shown in a face by the third module. The simulation results in PSPICE show that the proposed circuit system can learn, forget and form emotions like human. If the proposed circuit is further used on a humanoid robot platform through further research, the robot will have the ability of affective interaction with human so that it can be effectively used in affective company and other aspects.

Memristive Circuit Design of Emotional Generation and Evolution Based on Skin-Like Sensory Processor

Sensory processor in human skin is used for processing and transmitting sensations to the brain, which leads to body actions and emotional responses. In this paper, a memristive circuit of emotional generation and evolution based on skin-like sensory processor is proposed. The circuit includes: first, memristive skin-like sensory processor module; second, emotional generation and evolution module; and third, emotional expression module. The first module consists of four single-memristor skin-like sensory processors, which correspond to process sensations of pain, cold, warm, and tactile. It will automatically return to its initial state if sensory signals disappear. But if sensory signals are much strong, it will not automatically return to initial state unless applied "restoring signal" just like a surgical operation. The second module realizes a conversion mechanism from sensations to emotions using memristor as emotional synapse. Given signals from skin-like sensory processor, the memristance will decrease, which means the extent of emotion will increase, such as more happy. This is the emotional generation. The extent of emotion will be changed if the same sensation is applied to skin-like sensory processor repeatedly, which is the emotional evolution. The third module can show the generated emotions visually. The simulation results in PSPICE show that the proposed circuit can generate and evolve emotions like human beings after processing sensory signals from skin. The proposed circuit can be applied in a perceptual robot platform to realize the conversion from sensations to emotions, enabling the robot to have the ability to sense and process information.