Emulating Ebbinghaus forgetting behavior in a neuromorphic device based on low dimensional h-BN

Artificial synaptic devices that can mimic the biological synaptic functions of learning and forgetting are essential for the realization of neuromorphic computation, which could replace the von Neumann architecture. In this Letter, we have described a high-performing ultraviolet photodetector (wavelength 375 nm) using thin films of single-layer hexagonal boron nitride (h-BN) for potential use in fabricating a neuromorphic device. Furthermore, the classical Ebbinghaus forgetting curve can be optimized using various parameters such as the optical pulse width, number of pulses, and frequency of pulses. Our results show that the characteristic time constant (τ) has much more variability, indicating better performance control than the Ebbinghaus exponent (β). Furthermore, the performance of the optical synapse is very stable for low energy consumption, as low as 2–3 pJ

Artificial synapse based on carbon quantum dots dispersed in indigo molecular layer for neuromorphic applications

Conventional computers are limited in their performance due to the physical separation of the memory and processing units. To overcome this, parallel computation using artificial synapses has been thought of as a possible replacement in computing architecture. The development of nanoelectronic devices that can show synaptic functionalities is very important. Here, we report the robust synaptic functionalities of carbon quantum dots embedded in two terminal indigo-based organic synapses. The carbon quantum dots (CQDs) are prepared using an easy-to-do process from commercial jaggery. The CQDs have a size range between 3.5 and 4.5 nm with excellent light emission in the green region. CQD+indigo-based devices show extremely stable memory characteristics, with ON and OFF states differing by more than 10 Mohm. Devices show excellent long-term potentiation and long-term depression characteristics, with both synaptic weight updates following a double exponential behavior. The extent of nonlinearity is explained using the nonlinearity factor. The linear increase in memory is established with repeated learning and forgetting (or potentiation and depression) curves. This study gives a robust way to make an artificial synapse work efficiently at room temperature with excellent memory and synaptic behavior

Quantized Conductance and Multilevel Memory Operation in Mn3O4 Nanowire Network Devices Combined with Low Voltage Operation and Oxygen Vacancy Induced Resistive Switching

Abstract Quantum effects in nanowires and nanodevices can potentially revolutionize the device concepts with multi‐functionalities for future technologies. Memristive devices which undergo transition from high resistance state to low resistance state involve nanoscale conduction paths can show quantum effects at room temperature. Here, Mn3O4 nanowires based memristor showing very reliable resistive switching at very low voltages and with ON/OFF States ratio ∼ 103 is reported. The switching device can also be programmed to multiple memory states (up to 16 states ∼ 24). Since the conduction paths are geometrically constrained along the nanowires, quantized conductance steps are observed. Step‐wise conductance jumps are observed during the SET and RESET process with better control along RESET process. Conductance jumps range between 1 and 9 G0. The nanowire devices show very consistent resistive switching up to 100 °C. These measurements confirm extremely stable nanowire based resistive switching devices which can be used for next‐generation memories showing quantum effects in neuromorphic computing architectures

Bio-inspired artificial synapse for neuromorphic computing based on NiO nanoparticle thin film

Abstract The unprecedented need for data processing in the modern technological era has created opportunities in neuromorphic devices and computation. This is primarily due to the extensive parallel processing done in our human brain. Data processing and logical decision-making at the same physical location are an exciting aspect of neuromorphic computation. For this, establishing reliable resistive switching devices working at room temperature with ease of fabrication is important. Here, a reliable analog resistive switching device based on Au/NiO nanoparticles/Au is discussed. The application of positive and negative voltage pulses of constant amplitude results in enhancement and reduction of synaptic current, which is consistent with potentiation and depression, respectively. The change in the conductance resulting in such a process can be fitted well with double exponential growth and decay, respectively. Consistent potentiation and depression characteristics reveal that non-ideal voltage pulses can result in a linear dependence of potentiation and depression. Long-term potentiation (LTP) and Long-term depression (LTD) characteristics have been established, which are essential for mimicking the biological synaptic applications. The NiO nanoparticle-based devices can also be used for controlled synaptic enhancement by optimizing the electric pulses, displaying typical learning-forgetting-relearning characteristics

Bio-inspired Artificial synapse for neuromorphic computing based on NiO nanoparticle thin film

Abstract The unprecedented need for data processing in the modern technological era has created opportunities in neuromorphic devices and computation. This is primarily due to the extensive parallel processing done in our human brain. Data processing and logical decision-making at the same physical location is an exciting aspects of neuromorphic computation. For this, establishing reliable resistive switching devices working at room temperature with ease of fabrication is important. Here, a reliable analog resistive switching device based on Au/NiO nanoparticles/Au nanoparticles is discussed. The application of positive and negative voltage pulses of constant amplitude results in enhancement and reduction of synaptic current, which is consistent with potentiation and depression, respectively. The change in the conductance resulting in such a process can be fitted well with double exponential growth and decay, respectively. Consistent potentiation and depression characteristics reveal that non-ideal voltage pulses can result in a linear dependence of potentiation and depression with electric pulses. Long-term potentiation (LTP) and Long-term depression (LTD) characteristics have been established, which are essential for mimicking the biological synaptic applications. The NiO nanoparticle-based devices can also be used for controlled synaptic enhancement by optimizing the electric pulses, displaying typical learning-forgetting-relearning characteristics.</jats:p