Periodic Reporting for period 1 - RESWITCH (Redox-Controlled Resistive Switching in Hybrid Metal-Organic Thin Films towards Neuromorphic Computing)
Periodo di rendicontazione: 2019-04-01 al 2021-03-31
Artificial neural networks have revolutionized the field of artificial intelligence with human-like performance in fields such as computer vision and speech recognition already achieved. The ANNs take inspiration from the operation of biological neural networks, e.g. the human brain by employing layers of artificial neurons connected to each other via synaptic elements characterized by a weight factor. Yet, the von Neumann architecture of the present computing systems use opposite strategies compared to the brain: centralized versus distributed memory, and always-on versus event-driven systems. In practice this results in the software driven ANNs being orders of magnitude less energy efficient than the human brain, and the continuously growing demand for new machine learning models of increasing complexity account for a substantial part of the global energy consumption.
Neuromorphic computing presents a potential solution to these challenges by emulating the functionality and interconnectivity of the biological neural networks directly on hardware level. However, purely CMOS based neuromorphic circuits are impractical for the implementation of large networks, as even a single synapse or neuron can take tens of transistors each to implement. To accelerate the development of neuromorphic circuits, a next-generation device technology is needed. In practice, this equates to emulating the key features of the neurons and synapses directly on the materials’ level.
For the synaptic connections, a potential new technology are memristors that can emulate the synaptic weight through their history-dependent variable conductance. Various inorganic materials have been employed for these resistive switching devices that rely on the formation/dissolution of a conductive filament within an insulating matrix. The main challenges preventing widescale implementation are the device-to-device and cycle-to-cycle variability arising from the stochastic nature of the filament formation. Additionally, the typically high conductance in the ON-state results in high energy consumption.
The aim of the RESWITCH project is to enable a new type of resistive switching device concept based on metal-organic coordination polymer thin films. With redox-active ligands, the emulation of the synaptic weight can be based on the oxidation-state dependent properties of these thin films as the electronic conductivity can be modulated electrochemically with dynamic operation arising from the concurrent counter-ion motion. The interplay of the metal and organic constituents allows for precise control of the electric/electrochemical properties, but poor processability has been limiting their applicability for nanotechnology applications. The key element in RESWITCH is to implement a new thin film -based approach for conductive coordination polymers using Molecular Layer Deposition (MLD), a vapor phase thin film deposition method derived from the Atomic Layer Deposition (ALD) technique. As with ALD, it is defined by the sequential, self-saturating exposure of vapor-phase precursors onto surfaces. This monolayer accuracy in process control leads to sub-nanometer range precision in layer thickness control and in excellent uniformity over large area substrates,
The Objectives of the Project are three-fold:
1. Establishing novel MLD-chemistries based on redox-active organic ligands.
2. Gaining detailed understanding on the link between the thin film composition and it’s redox-properties and electrical conductivity.
3. Integration of the newly developed materials in a resistive switching device demonstrator with artificial synapse -like functionality.
Neuromorphic computing presents a potential solution to these challenges by emulating the functionality and interconnectivity of the biological neural networks directly on hardware level. However, purely CMOS based neuromorphic circuits are impractical for the implementation of large networks, as even a single synapse or neuron can take tens of transistors each to implement. To accelerate the development of neuromorphic circuits, a next-generation device technology is needed. In practice, this equates to emulating the key features of the neurons and synapses directly on the materials’ level.
For the synaptic connections, a potential new technology are memristors that can emulate the synaptic weight through their history-dependent variable conductance. Various inorganic materials have been employed for these resistive switching devices that rely on the formation/dissolution of a conductive filament within an insulating matrix. The main challenges preventing widescale implementation are the device-to-device and cycle-to-cycle variability arising from the stochastic nature of the filament formation. Additionally, the typically high conductance in the ON-state results in high energy consumption.
The aim of the RESWITCH project is to enable a new type of resistive switching device concept based on metal-organic coordination polymer thin films. With redox-active ligands, the emulation of the synaptic weight can be based on the oxidation-state dependent properties of these thin films as the electronic conductivity can be modulated electrochemically with dynamic operation arising from the concurrent counter-ion motion. The interplay of the metal and organic constituents allows for precise control of the electric/electrochemical properties, but poor processability has been limiting their applicability for nanotechnology applications. The key element in RESWITCH is to implement a new thin film -based approach for conductive coordination polymers using Molecular Layer Deposition (MLD), a vapor phase thin film deposition method derived from the Atomic Layer Deposition (ALD) technique. As with ALD, it is defined by the sequential, self-saturating exposure of vapor-phase precursors onto surfaces. This monolayer accuracy in process control leads to sub-nanometer range precision in layer thickness control and in excellent uniformity over large area substrates,
The Objectives of the Project are three-fold:
1. Establishing novel MLD-chemistries based on redox-active organic ligands.
2. Gaining detailed understanding on the link between the thin film composition and it’s redox-properties and electrical conductivity.
3. Integration of the newly developed materials in a resistive switching device demonstrator with artificial synapse -like functionality.
In the MLD-process development phase, a family of deposition processes for high-quality 1D coordination polymer thin films based on dithiooxamide derivatives was developed with Cu and Ni as the metal center. In all cases, high out-of-plane ordering is observed in the resultant thin films suggesting the formation of a well-ordered secondary structure by the parallel alignment of the 1D polymer chains. Investigation into their electrical and electrochemical properties revealed that the electrical conductivity of the films can be adjusted up 8 orders of magnitude from nearly insulating up to 10 mS/cm by reductive doping. Furthermore, the dopant induced conductance change is associated with a semiconductor-to-metal transition. First principles studies were conducted to complement the experimental findings. They point to the formation of a half-filled conduction band that arises from the increased interchain interaction and the formation of extended (−Cu–S−) conduction pathways. These results indicate that the key in the formation of the conductive pathways is the superior quality of the MLD thin films compared to other synthesis methods.
The applicability of these materials towards resistive switching devices was demonstrated. The memristive properties of arise from the changing oxidation states of the adjacent layers when the device is operated in a nanobattery-like fashion. This redox-reaction based operation could be harnessed for excellent cycle-to-cycle performance as the switching threshold voltage is governed by the materials’ intrinsic reduction potential in contrast to the stochastic filament formation in the inorganic counterparts. The relatively low overall conductance of the materials resulted in appreciably high device resistance for both the Off- and On-states, highly desirable for low-power operation. Furthermore, the device design allows for tailoring the switching threshold voltage, the On and Off -state resistance, and the volatility of the memory state by combining different material pairs. Aside from the conductance tuning, the redox reaction rate, and thus the rate of change in the device conductance is dependent on the excitation frequency and amplitude, thereby emulating the time-dependent dynamics of biological synapses such as spike-rate and spike timing dependent plasticity (SRDP and STDP). The mixed ion/electron conduction in these materials allowed for the realization of concurrent short- and long-term memory in a single device.
The applicability of these materials towards resistive switching devices was demonstrated. The memristive properties of arise from the changing oxidation states of the adjacent layers when the device is operated in a nanobattery-like fashion. This redox-reaction based operation could be harnessed for excellent cycle-to-cycle performance as the switching threshold voltage is governed by the materials’ intrinsic reduction potential in contrast to the stochastic filament formation in the inorganic counterparts. The relatively low overall conductance of the materials resulted in appreciably high device resistance for both the Off- and On-states, highly desirable for low-power operation. Furthermore, the device design allows for tailoring the switching threshold voltage, the On and Off -state resistance, and the volatility of the memory state by combining different material pairs. Aside from the conductance tuning, the redox reaction rate, and thus the rate of change in the device conductance is dependent on the excitation frequency and amplitude, thereby emulating the time-dependent dynamics of biological synapses such as spike-rate and spike timing dependent plasticity (SRDP and STDP). The mixed ion/electron conduction in these materials allowed for the realization of concurrent short- and long-term memory in a single device.
The projected low energy consumption, and especially the versatility of the concept devices produced during this Project demonstrate the high potential of coordination polymer thin films towards neuromorphic applications. Ultimately for this purpose it is not sufficient to merely construct bio-plausible synapses, but their properties have to be adjustable to meet the requirements of any underlying mathematical framework. Due to the physical nature of the switching, the previous inorganic devices are rather inflexible in this sense, and the only option is the often serendipitous discovery of new materials. The outcome of this Project presents an attractive alternative, where the device properties can directly be tailored to meet the required parameters by selecting the suitable device layout from a library of coordination polymer thin films. The time-dependent characteristics of the devices introduced in this project are an intriguing avenue towards more bio-realistic AI technology such as spiking neural networks.