Transforming the short-term sensing stimuli to long-term e-skin memory

Abstract

Abstract-A Tantalum Pentoxide (Ta2O5) based resistive nonvolatile memory device with bipolar switching behaviour was developed to demonstrate the new concept of memory in e-skin. The memory device showed stable switching behavior under preprogrammed voltage stimuli after an initial forming process. The memory cell was then integrated with a commercial tactile sensor with a new interface circuit, which enabled the switching of the memory cell through the electrical output from the sensor. This study provides a novel method for handling the transport and storage of large tactile data and will trigger advances towards memorable e-skin

Human somatosensory systems based on sensor-memory-integrated technology

“…13 (a) Schematic of the sensor-memristor interface circuit. 151 Copyright: 2023 IEEE. (b) Memristive skin-like sensory processor module consisting of four single-memristor skin-like sensory processor circuits to receive and process sensations of pain, cold, warm, and tactile, respectively.…”

“…To date, thousands of outstanding achievements have been made promoting tactile storage technology. Many developments in mechano-sensor-memory-integrated technology have been made in the directions of non-volatility, 39 biomimics 15,33,34,40,41 (as shown in Fig. 4a), visualization 42,44 (as shown in Fig.…”

“…It is essential to develop layered manufacturing on-chip integration technology for future high-performance, low-energy, sensing computing unified systems for sensing, storage, snf computing vertical superposition. Liu et al 151 introduced a new concept of memory in an e-skin system: being constructed by tantalum pentoxide (Ta 2 O 5 ) and based on a nonvolatile memristor with a bipolar switching characteristic. Every memristor unit was incorporated with a commercial tactile sensor, forming a new interface circuit.…”

Fingerprint‐Enhanced Capacitive‐Piezoelectric Flexible Sensing Skin to Discriminate Static and Dynamic Tactile Stimuli

“…Some of the present eSkins have used machine learning tools also to decipher the information from the data acquired by tactile sensors . In fact, machine learning to handle tactile data and neuromimicking hardware architectures has been gaining lot of interest recently[4b,5] along with local data storage . While multifunctional sensing is an attractive attribute of eSkin, suitable pressure or force sensing is the most important for critical static/dynamic force feedback in robotics to execute various manipulation tasks .…”