Search for: All records

Abstract Promising synaptic behaviour has been exhibited by memristors based on natural organic materials. Such memristor‐based neuromorphic systems offer notable benefits, including environmental sustainability, low production and disposal costs, non‐volatile storage capability, and bio/Complementary Metal‐Oxide‐Semiconductor (CMOS) compatibility. Here, a 256‐level honey memristor‐based neuromorphic system is experimentally evaluated for image recognition. In detail, first, 256‐level honey memristors are manufactured and tested based on in‐house technology; next, the non‐linear characteristics and inherent variation of honey memristor devices, which lead to imprecise weight updates and limit the inference accuracy, are investigated. Experimental results indicate that the inference accuracy of the 256‐level honey memristor‐based neuromorphic system is greater than 88% without cycle‐to‐cycle variations and 87% with cycle‐to‐cycle variations for different optimization algorithms. The overall performance of optimization algorithms with and without variation is compared in terms of energy and latency, where the momentum algorithm consistently outperforms the rest of the algorithms. This 256‐level honey memristor is a promising alternative enabling sustainable neuromorphic systems, encouraging further research into natural organic materials for neuromorphic computing. 

Nowadays, non-volatile memory technologies have been widely applied in different areas. Of these memory technologies, non-volatile resistive random access memory (ReRAM) is attractive because of its simple device architecture and fabrication process, high scalability and data density, good performances in terms of switching speed, high power efficiency and reasonably wide memory window. In order to address the issues of disposable and degradation of electronic waste by typical ReRAM with the active layer made of inorganic oxide materials and fossil-fuel based polymeric materials, a green and sustainable strategy has been adopted in producing ReRAM by using natural organic-based materials based on protein and carbohydrate, such as honey, fructose, aloe vera, etc. Among these materials, pectin-polysaccharide thin film has demonstrated promising resistive switching characteristics. The two ranges of pectin concentrations that have been investigated are ³5 mg/ml and £1.5 mg/ml, and it showed that pectin with concentration <1.5 mg/ml reveals a higher ON/OFF ratio. However, the resistive switching characteristics with pectin concentration between 1.5 mg/ml and 5 mg/ml have yet been explored and reported. In this work, pectin with concentrations of 1.5~5 mg/ml were prepared from pectin-polysaccharide solution into the active switching layer, and ReRAM devices with such pectin resistive switching layer were fabricated. The pectin-polysaccharide solution, pectin resistive film, and ReRAM devices were systematically investigated. Surface tension and contact angle of pectin-polysaccharide precursor solutions as a function of pectin concentration on the substrate were measured by a goniometer. Surface topography of solidified thin films was characterized by an atomic force microscope (AFM) and a field-emission scanning electron microscope (FE-SEM). Chemical functional groups of the pectin-polysaccharide precursor solutions and solidified thin films were examined by a Fourier transform infrared (FTIR) spectroscopy. The resistive switching behaviors were characterized and compared by electrical measurement. The results show that 4 mg/ml recorded the highest ON/OFF ratio compared to ever reported values, as well as desirable memory window, non-volatility in retention, and stability over 100 cycles. This study proves that pectin-polysaccharide is a promising green and sustainable bio-organic material for non-volatile ReRAM for electronic applications such as in emerging neuromorphic computing systems.