tivity and component density, with dozens of transistors 43 required per neuron and additional external memories 44 needed for synaptic weights. This results in several µm 45 large neurons. Since dedicated wiring for every synap-46 tic link is not practical, neuromorphic electronic systems 47 usually employ a shared digital communication bus with 48 time-division multiplexing [5], gaining interconnectivity 49 at the expense of bandwidth, or use schemes such as 50 address-event representation (AER) [6]. As an alterna-51 tive, hardware technologies relying on physics for neuro-52 morphic computation are nowadays gaining increasing re-53 search interest. These include hybrid CMOS/memristive 54 systems (see [2] for an overview), spintronics [7] and pho-55 tonic systems [8, 9]. 56 57 Neuromorphic photonics is a nascent field, recently 58 gaining significant traction due to increasing importance 59 of AI algorithms and rapid advances in the field of pho-60 tonic integrated circuits (PICs). Optoelectronic systems 61 in particular are considered as highly suitable for future 62 cognitive computing hardware, as they benefit from op-63 eration with both electrons and photons, each excelling 64 at different key functionalities [14]. Thanks to their ca-65 pability to address bandwidth and interconnect energy 66 limits in a scalable fashion, optoelectronic systems might 67 prove as the optimal solution to overcome these limita-68 tions [15]. There are many different approaches to re-69 alization of artificial neural networks in optics (see for 70 example review [16]). Using delayed feedback, recurrent 71 neural networks can be realized in a photonic reservoir 72 computer, yielding networks with large number of virtual 130 and operation of RTDs with delayed feedback [42], ad-131 dressing only operation of a single (solitary) device. In 132 this work, we investigate interconnected systems con-133 sisting of multiple independent RTD-based monolithic 134 integrated optoelectronic nodes. We employ the nodes 135 as stateless excitable devices and take advantage of the 136 spike-based signalling to implement information process-137 ing tasks and multi-device networks with prospects for 138 very low footprint, low energy and high-speed operation 139 due to the use of sub-λ elements. We utilize two types 140 of nodes: an electronic-optical (E/O) RTD-LD system, 141 realized with a RTD element coupled to a nanoscale laser 142 diode (LD), and an optical-electronic (O/E) RTD-PD 143 system, realized with a photodiode (PD) coupled to a 144 RTD element. In both node types, spiking threshold can 145 be adjusted via bias voltage tuning. An illustration of 146 two nodes with an unidirectional optical weighted link, 147 representing two feedfoward linked neurons, is depicted 148 in Fig. 1a. 149 A. Optoelectronic RTD-system architecture 150 In both the RTD-LD and RTD-PD nodes, the two 151 functional blocks are integrated in a monolithic, metal 152 dielectric cavity micro-pillar with DBQW regions on 153 GaAs/AlGaAs materials [43] for operation at ...

The solubility of 1-keto-1,2,3,4-tetrahydro-6-methylcarbazole (KTMC) in acetone in the temperature range from 18 to 55 °C was determined by the residue solid technique and the polythermal method using a heat flow calorimeter with turbidity measurement capacities. The results were correlated using a polynomial equation. Utilizing the polythermal method, the metastable zone has also been determined. The broadening of the metastable zone with increasing cooling was observed from 0.05 to 2.0 °C·min-1.

With the recent development of artificial intelligence and deep neural networks, alternatives to the Von Neumann architecture are in demand to run these algorithms efficiently in terms of speed, power and component size. In this theoretical study, a neuromorphic, optoelectronic nanopillar metal-cavity consisting of a resonant tunneling diode (RTD) and a nanolaser diode (LD) is demonstrated as an excitable pulse generator. With the proper configuration, the RTD behaves as an excitable system while the LD translates its electronic output into optical pulses, which can be interpreted as bits of information. The optical pulses are characterized in terms of their width, amplitude, response delay, distortion and jitter times. Finally, two RTD-LD units are integrated via a photodetector and their feasibility to generate and propagate optical pulses is demonstrated. Given its low energy consumption per pulse and high spiking rate, this device has potential applications as building blocks in neuromorphic processors and spiking neural networks.

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