Timing Dependent Plasticity Research Articles

Sustainable vertically-oriented graphene-electrode memristors for neuromorphic applications

Neuromorphic computing, an innovative field in electronic and computing engineering, aims to enhance computing paradigms by simulating brain processes. Memristors, a two-terminal device, hold promise in revolutionising neuromorphic architectures by circumventing the Von-Neumann bottleneck. The performance and applicability of memristors heavily rely on the materials and fabrication processes employed. Graphene exhibits unique properties that can be leveraged in memristor design. Moreover, graphene stands out as a material with the potential for large-scale, sustainable production through Plasma Enhanced Chemical Vapour Deposition (PECVD). Notably, the properties of graphene-electrode memristors vary with minor structural differences induced by different PECVD temperatures. This paper reports the synthesis of graphene electrodes by time- and cost-effective PECVD from a sustainable plant extract for memristors. In addition, this paper delves into investigating how these structural variations impact the properties of graphene memristors and explores their potential exploitation in neuromorphic applications for implementing the well-known Spike Timing Dependent Plasticity (STDP) learning mechanism. The paper also utilises the developed STDP learning to perform an unsupervised spike-based pattern classification task.

A Self-Driven Ga2O3 Memristor Synapse for Humanoid Robot Learning.

In recent years, the rapid development of brain-inspired neuromorphic systems has created an imperative demand for artificial photonic synapses that operate with low power consumption. In this study, a self-driven memristor synapse based on gallium oxide (Ga2O3) nanowires is proposed and demonstrated successfully. This memristor synapse is capable of emulating a range of functionalities of biological synapses when exposed to 255nm light stimulation. These functionalities encompass peak time-dependent plasticity, pulse facilitation, and memory learning capabilities. It exhibits an ultrahigh paired-pulse facilitation index of 158, indicating exceptional learning performance. The transition from short-term memory to long-term memory can be attributed to the remarkable relearning capabilities. Furthermore, the potential applications of the memristor synapse is showcased through the successful manipulation of a humanoid intelligent robot. Upon establishing artificial intelligence (AI) systems, the control commands originating from the synaptic device can drive the humanoid robot to perform various actions. Based on the memristor synapses, the autonomous feedback system of the humanoid robot facilitates a good collaboration between robotic actions and bio-inspired light perception. Therefore, this research opens up an effective way to advance the development of neuromorphic computing technologies, AI systems, and intelligent robots that demand ultra-low energy consumption.

Supervised learning of spatial features with STDP and homeostasis using Spiking Neural Networks on SpiNNaker

Artificial Neural Networks (ANN) have gained significant popularity thanks to their ability to learn using the well-known backpropagation algorithm. Conversely, Spiking Neural Networks (SNNs), despite having broader capabilities than ANNs, have always posed challenges in the training phase. This paper shows a new method to perform supervised learning on SNNs, using Spike Timing Dependent Plasticity (STDP) and homeostasis, aiming at training the network to identify spatial patterns. Spatial patterns refer to spike patterns without a time component, where all spike events occur simultaneously. The method is tested using the SpiNNaker digital architecture. A SNN is trained to recognise one or multiple patterns and performance metrics are extracted to measure the performance of the network. Some considerations are drawn from the results showing that, in the case of a single trained pattern, the network behaves as the ideal detector, with 100% accuracy in detecting the trained pattern. However, as the number of trained patterns on a single network increases, the accuracy of identification is linked to the similarities between these patterns. This method of training an SNN to detect spatial patterns may be applied to pattern recognition in static images or traffic analysis in computer networks, where each network packet represents a spatial pattern. It will be stipulated that the homeostatic factor may enable the network to detect patterns with some degree of similarity, rather than only perfectly matching patterns. The principles outlined in this article serve as the fundamental building blocks for more complex systems that utilise both spatial and temporal patterns by converting specific features of input signals into spikes. One example of such a system is a computer network packet classifier, tasked with real-time identification of packet streams based on features within the packet content.

Flexible Artificial Ag NPs:a-SiC0.11:H Synapse on Al Foil with High Uniformity and On/Off Ratio for Neuromorphic Computing.

A neuromorphic computing network based on SiCx memristor paves the way for a next-generation brain-like chip in the AI era. Up to date, the SiCx-based memristor devices are faced with the challenge of obtaining flexibility and uniformity, which can push forward the application of memristors in flexible electronics. For the first time, we report that a flexible artificial synaptic device based on a Ag NPs:a-SiC0.11:H memristor can be constructed by utilizing aluminum foil as the substrate. The device exhibits stable bipolar resistive switching characteristic even after bending 1000 times, displaying excellent flexibility and uniformity. Furthermore, an on/off ratio of approximately 107 can be obtained. It is found that the incorporation of silver nanoparticles significantly enhances the device's set and reset voltage uniformity by 76.2% and 69.7%, respectively, which is attributed to the contribution of the Ag nanoparticles. The local electric field of Ag nanoparticles can direct the formation and rupture of conductive filaments. The fitting results of I-V curves show that the carrier transport mechanism agrees with Poole-Frenkel (P-F) model in the high-resistance state, while the carrier transport follows Ohm's law in the low-resistance state. Based on the multilevel storage characteristics of the Al/Ag NPs:a-SiC0.11:H/Al foil resistive switching device, we successfully observed the biological synaptic characteristics, including the long-term potentiation (LTP), long-term depression (LTD), and spike-timing-dependent plasticity (STDP). The flexible artificial Ag NPs:a-SiC0.11:H/Al foil synapse possesses excellent conductance modulation capabilities and visual learning function, demonstrating the promise of application in flexible electronics technology for high-efficiency neuromorphic computing in the AI period.

Parallel hyperparameter optimization of spiking neural networks

Hyperparameter optimization of spiking neural networks (SNNs) is a difficult task which has not yet been deeply investigated in the literature. In this work, we designed a scalable constrained Bayesian based optimization algorithm that prevents sampling in non-spiking areas of an efficient high dimensional search space. These search spaces contain infeasible solutions that output no or only a few spikes during the training or testing phases, we call such a mode a “silent network”. Finding them is difficult, as many hyperparameters are highly correlated to the architecture and to the dataset. We leverage silent networks by designing a spike-based early stopping criterion to accelerate the optimization process of SNNs trained by spike timing dependent plasticity and surrogate gradient. We parallelized the optimization algorithm asynchronously, and ran large-scale experiments on heterogeneous multi-GPU Petascale architecture. Results show that by considering silent networks, we can design more flexible high-dimensional search spaces while maintaining a good efficacy. The optimization algorithm was able to focus on networks with high performances by preventing costly and worthless computation of silent networks.

Neuromorphic learning and recognition in WO3−x thin film-based forming-free flexible electronic synapses

In pursuing advanced neuromorphic applications, this study introduces the successful engineering of a flexible electronic synapse based on WO3-x, structured as W/WO3-x/Pt/Muscovite-Mica. This artificial synapse is designed to emulate crucial learning behaviors fundamental to in-memory computing. We systematically explore synaptic plasticity dynamics by implementing pulse measurements capturing potentiation and depression traits akin to biological synapses under flat and different bending conditions, thereby highlighting its potential suitability for flexible electronic applications. The findings demonstrate that the memristor accurately replicates essential properties of biological synapses, including short-term plasticity (STP), long-term plasticity (LTP), and the intriguing transition from STP to LTP. Furthermore, other variables are investigated, such as paired-pulse facilitation, spike rate-dependent plasticity, spike time-dependent plasticity, pulse duration-dependent plasticity, and pulse amplitude-dependent plasticity. Utilizing data from flat and differently bent synapses, neural network simulations for pattern recognition tasks using the Modified National Institute of Standards and Technology dataset reveal a high recognition accuracy of ∼95% with a fast learning speed that requires only 15 epochs to reach saturation.

Tunable memory behavior in light stimulated artificial synapse based on ZnO thin film transistors

Abstract Optoelectronic synapses are inevitable for realizing neuromorphic vision systems, which require the integration of image recognition, memory and image processing into a single platform. In this work, we present a three terminal optoelectronic synapse created using zinc oxide (ZnO) thin film transistor. The persistent photoconductivity (PPC) of ZnO thin film is utilized to demonstrate the synaptic behaviour. The change in conductance of the device under UV illumination has been interpreted as the weight change in the synapse. The basic synaptic functions such as sensory memory, short term memory, long term memory, duration time dependent plasticity and paired pulse facilitation have been successfully demonstrated. The device shows a PPF index of 160\%, comparable to other optoelectronic synapses reported in literature. Further, to corroborate the existing theory that PPC is caused by oxygen vacancies, additional characterizations are carried out and the presence of oxygen vacancies is detected in the fabricated ZnO device. Subsequently, pattern recognition of MNIST handwritten dataset has been performed using the conductance tuning curves of the proposed ZnO TFT based synapses in a neural network architecture, thereby demonstrating their feasibility to be used in neuromorphic applications.

Assessment of mineral compositions on geo-mechanical time dependent plastic creep deformation

Understanding rock mechanical time-dependent plastic deformation enables the researchers to accurately predict rock responses during loading and unloading. This research focuses on the geomechanical aspects of hydrogen storage, particularly rock resistance to creep deformation under varying stresses and pressures. Modelling creep deformation in underground storage mediums has been overlooked and requires further consideration, as it is a critical, cyclical, time-dependent phenomenon essential for hydrogen containment. The study's novelty is demonstrated in its focus on geomechanical properties of rock minerals, providing insights that aid in site-selection criteria for hydrogen storage A creep deformation model with varying mineral compositions is used to assess creep deformation strain. In homogenous cases, calcite showed the highest resistance to creep deformation after three loading cycles, primarily due to its high Young's Modulus. As for the heterogenous cases, the results indicated that the mixture of calcite and quartz was the most creep-resistant rock composition. In terms of rock types, basalt samples demonstrated the highest resistance to creep deformation, while siltstone exhibited the highest susceptibility to deformation. It was observed that as the number of cycles increase, the rock mechanical stability decreases, hence optimizing the number of cycles is a must to ensure hydrogen geo-containment.

Memristor based on carbon nanotube gelatin composite film as artificial optoelectronic synapse for image processing

Photoelectric artificial synapses based on memristors is an effective method to realize neuromorphic computation. This study presents an optoelectronic responsive artificial synapse made of a composite material consisting of gelatin and carbon nanotubes. The memristor demonstrates characteristics of analog resistive switching, the ability to store multiple memory states, and impressive retention properties. It has the capability to induce an excitatory post-synaptic current by means of electrical pulses or pulsed light exposure. The excitatory post-synaptic current can be modulated by the number, amplitude and interval of electrical pulses, as well as the action time, interval and light intensity of optical pulses. The artificial synapse showcases the emulation of fundamental Hebbian learning protocols, including spike timing dependent plasticity and spike amplitude dependent plasticity. In addition, the charge transfer in the carbon nanotube gelatin composite optoelectronic memristor is investigated through first-principles calculations, shedding light on its operational mechanism. Experimental results show that these devices have the potential to be utilized for processing image information, resulting in a significant reduction of input data and training expenses when recognizing handwritten numbers. Overall, the optoelectronic synapse exhibits promising image processing prospects in the field of neuromorphic computing.

Spike-enhanced synapse functions of SnOx-based resistive memory

This paper explores the application of resistive random-access memory (RRAM) devices in emulating various synapse functions. The electrical properties of an indium tin oxide (ITO)/SnOx/TaN device are examined, including the activation process, switching process, endurance (>102), and retention characteristics (104 s). A conduction mechanism for the resistive-switching process based on the migration of oxygen ions in the insulating SnOx film is proposed. Furthermore, the multilevel cell characteristics of the device, demonstrating the control of resistance states by variations in reset voltage and compliance current, were found to be suitable, increasing its data storage capacity. Synapse functions, including potentiation and depression, are tested, and their repeatability is evaluated. A neural network-based Modified National Institute Standards and Technology pattern recognition system is applied to the ITO/SnOx/TaN device, showcasing its capabilities in pattern recognition tasks. This study further explores various synapse functions, such as paired-pulse facilitation, paired-pulse depression, excitatory postsynaptic current, and activity-dependent synaptic plasticity. Hebbian learning rules, including spike rate-dependent plasticity, are demonstrated, along with spike time-dependent plasticity under various spike types, making it suitable for high-functionality neuromorphic applications. Finally, we assembled 3 × 3 synapse arrays and utilized them to form recognizable patterns, demonstrating the potential of the device in implementing neural network functions.

Induction of Synaptic Plasticity in Flexible Organic Synaptic Transistors with Cross‐Linked Polymer Dielectric

AbstractControlled cross‐linking of polymer dielectric poly (4‐vinylphenol) (PVP) is demonstrated as an effective tool in enhancing the performance of flexible organic synaptic transistors (OSTs). Investigation of variation of concentration of the cross‐linking agent methylated poly (melamine‐co‐formaldehyde) (PMCF) in PVP in bilayer combination with high‐k hafnium oxide (HfO2) as gate dielectric in devices shows that the lower concentration of cross‐linking agent results in better memory performance. OSTs with 26% PMCF concentration in PVP (by mass) exhibit excellent memory performance with memory window > 4 V for VGS sweep of ±5 V, static retention of ≈104 s, dynamic retention for 500 cycles, and ≈125 continuous program/erase cycles. Pulse paired facilitation with relaxation time constants of 370 and 4670 ms respectively for slow and rapid phases with regulating modulation amplitude of ≈1 resemble a biological synapse. Through excitatory post synaptic current characteristics, spike timing dependant plasticity and spike voltage dependant plasticity are clearly observed, with low energy consumption per spike on the order of 10 pJ. Further, by leveraging the intricate interconnected data transfer and computation phenomenon, “AND” logic is effectively implemented using these OSTs. These exciting results may open up new directions toward the development of hardware for neuromorphic computing.

Bi-sigmoid spike-timing dependent plasticity learning rule for magnetic tunnel junction-based SNN.

In this study, we explore spintronic synapses composed of several Magnetic Tunnel Junctions (MTJs), leveraging their attractive characteristics such as endurance, nonvolatility, stochasticity, and energy efficiency for hardware implementation of unsupervised neuromorphic systems. Spiking Neural Networks (SNNs) running on dedicated hardware are suitable for edge computing and IoT devices where continuous online learning and energy efficiency are important characteristics. We focus in this work on synaptic plasticity by conducting comprehensive electrical simulations to optimize the MTJ-based synapse design and find the accurate neuronal pulses that are responsible for the Spike Timing Dependent Plasticity (STDP) behavior. Most proposals in the literature are based on hardware-independent algorithms that require the network to store the spiking history to be able to update the weights accordingly. In this work, we developed a new learning rule, the Bi-Sigmoid STDP (B2STDP), which originates from the physical properties of MTJs. This rule enables immediate synaptic plasticity based on neuronal activity, leveraging in-memory computing. Finally, the integration of this learning approach within an SNN framework leads to a 91.71% accuracy in unsupervised image classification, demonstrating the potential of MTJ-based synapses for effective online learning in hardware-implemented SNNs.

On the Dynamic Mechanical Behavior of Wrought and AM 17-4 Precipitation-Hardenable Stainless Steels Under Rapid Heating

Dynamic large-strain plasticity problems in metals can produce temperatures high enough to alter the microstructure, but the limited time-at-temperature prevents complete transformation, thereby making the material strength time-dependent. Precipitation reactions (age-hardening) are an important class of transformations that can create time-dependent dynamic plasticity under rapid heating and loading. This work explores the dynamic behavior of a precipitation-hardenable stainless steel (17-4) produced by wrought and Additive Manufacturing (AM) methods with a rapidly-heated Kolsky bar technique. Wrought 17-4, a martensitic stainless steel, is examined in three common heat treatments (solution-treated, peak-aged and over-aged) at temperatures up to 1000 °C and heating times limited to about three seconds. Solution-treated wrought 17-4 is observed to thermally-harden at aging temperatures (> 400 °C) due to rapid precipitate growth. Peak-aged precipitation strengthening becomes ineffective above 550 °C, as peak-aged material becomes indistinguishable from the solution treated-condition. Over-aged wrought 17-4 does not behave like either of the other conditions, owing to the effect of the extended heat treatment on the precipitates and on the martensite matrix. Stress-relieved AM 17-4 exhibits high dynamic strength and strain hardening at room-temperature due to its meta-stable austenite content and partial age-hardening during the build or stress-relief treatment. A plasticity model is developed for solution-treated wrought 17-4 that captures time-dependent aging effects that are derived from separate aging kinetics experiments. A separate model is developed for over-aged wrought 17-4 that contains no time-dependence as the precipitate population in this material appears to be more stable under rapid heating.

Synapse Neurotransmitter Channel-Inspired AlOx Memristor with "V" Type Oxygen Vacancy Distribution.

Memristor possesses great potential and advantages in neuromorphic computing, while consistency and power consumption issues have been hindering its commercialization. Low cost and accuracy are the advantages of human brain, so memristors can be used to construct brain-like synaptic devices to solve these problems. In this work, a five-layer AlOx device with a V-shaped oxygen distribution is used to simulate biological synapses. The device simulates synapse structurally. Further, under electrical stimulation, O2- moves to the Ti electrode and oxygen vacancy (Vo) moves to the Pt electrode, thus forming a conductive filament (CF), which simulates the Ca2+ flow and releases neurotransmitters to the postsynaptic membrane, thus realizing the transmission of information. By controlling applied voltage, the regulation of Ca2+ gated pathway is realized to control the Ca2+ internal flow and achieve different degrees of information transmission. Long-term Potentiation (LTP)/Long-term Depression (LTD), Spike Timing Dependent Plasticity (STDP), these basic synaptic performances can be simulated. The AlOx device realizes low power consumption (56.7 pJ/392 fJ), high switching speed (25ns/60ns), and by adjusting the window value, the nonlinearity is improved (0.133/0.084), a high recognition accuracy (98.18%) is obtained in neuromorphic simulation. It shows a great prospect in multi-value storage and neuromorphic computing.

A recurrence model capturing interface traps for non-zero bandgap GFETs towards dynamic mimicking of synaptic plasticity

This paper primarily focusses on developing an analytical model with a non-zero bandgap of boron (B)/nitrogen (N) substitution doped graphene field-effect transistors (GFETs) to mimic the synaptic behaviour. The trap charges at the channel and gate-insulator interface are utilized to induce the hysteresis conduction mechanism, which is further exploited to accomplish synaptic plasticity. The proposed recurrence, that is the time-dependent trap drain current model, accurately captures the physical insights of trap charges using an equivalent metal–insulator–graphene model. An interesting feature of the proposed model is that it is compatible with both the doped (B/N) and the undoped GFETs. The model is also investigated to generate the hysteresis characteristics of the GFET that are further utilized to simulate the synaptic behaviour. Another fact that must be noticed is the existence of complete OFF regions for doped B/N GFETs, unlike the undoped case, which manifest undesirable ambipolar behaviour. The synapse made up of B/N-doped GFETs predicts an optimistic learning and memory mechanism, termed as spike time-dependent plasticity (STDP). The STDP characteristics of B/N doped synaptic GFETs have been enhanced by more than 18 × compared to artificial synapses made of undoped GFETs. Hence, the hysteresis behaviour along with the non-zero bandgap of B/N substitution doped GFETs makes them highly favourable for the dynamic mimicking of synaptic plasticity to be efficiently biologically plausible.

Control-Etched Ti3C2Tx MXene Nanosheets for a Low-Voltage-Operating Flexible Memristor for Efficient Neuromorphic Computation.

Hardware neural networks with mechanical flexibility are promising next-generation computing systems for smart wearable electronics. Overcoming the challenge of developing a fully synaptic plastic network, we demonstrate a low-operating-voltage PET/ITO/p-MXene/Ag flexible memristor device by controlling the etching of aluminum metal ions in Ti3C2Tx MXene. The presence of a small fraction of Al ions in partially etched MXene (p-Ti3C2Tx) significantly suppresses the operating voltage to 1 V compared to 7 V from fully Al etched MXene (f-Ti3C2Tx)-based devices. Former devices exhibit excellent non-volatile data storage properties, with a robust ∼103 ON/OFF ratio, high endurance of ∼104 cycles, multilevel resistance states, and long data retention measured up to ∼106 s. High mechanical stability up to ∼73° bending angle and environmental robustness are confirmed with consistent switching characteristics under increasing temperature and humid conditions. Furthermore, a p-Ti3C2Tx MXene memristor is employed to mimic the biological synapse by measuring the learning-forgetting pattern for ∼104 cycles as potentiation and depression. Spike time-dependent plasticity (STDP) based on Hebb's Learning rules is also successfully demonstrated. Moreover, a remarkable accuracy of ∼95% in recognizing modified patterns from the National Institute of Standards and Technology (MNIST) data set with just 29 training epochs is achieved in simulation. Ultimately, our findings underscore the potential of MXene-based flexible memristor devices as versatile components for data storage and neuromorphic computing.

A fully spiking coupled model of a deep neural network and a recurrent attractor explains dynamics of decision making in an object recognition task.

Objective.Object recognition and making a choice regarding the recognized object is pivotal for most animals. This process in the brain contains information representation and decision making steps which both take different amount of times for different objects. While dynamics of object recognition and decision making are usually ignored in object recognition models, here we proposed a fully spiking hierarchical model, explaining the process of object recognition from information representation to making decision.Approach.Coupling a deep neural network and a recurrent attractor based decision making model beside using spike time dependent plasticity learning rules in several convolutional and pooling layers, we proposed a model which can resemble brain behaviors during an object recognition task. We also measured human choices and reaction times in a psychophysical object recognition task and used it as a reference to evaluate the model.Main results.The proposed model explains not only the probability of making a correct decision but also the time that it takes to make a decision. Importantly, neural firing rates in both feature representation and decision making levels mimic the observed patterns in animal studies (number of spikes (p-value < 10-173) and the time of the peak response (p-value < 10-31) are significantly modulated with the strength of the stimulus). Moreover, the speed-accuracy trade-off as a well-known characteristic of decision making process in the brain is also observed in the model (changing the decision bound significantly affect the reaction time (p-value < 10-59) and accuracy (p-value < 10-165)).Significance.We proposed a fully spiking deep neural network which can explain dynamics of making decision about an object in both neural and behavioral level. Results showed that there is a strong and significant correlation (r= 0.57) between the reaction time of the model and of human participants in the psychophysical object recognition task.

Memory device based on MoS2-polyvinyl alcohol for simulating synaptic behavior

A memory device based on MoS2-polyvinyl alcohol was fabricated. As a good dispersant, polyvinyl alcohol solution can disperse 2D layered materials more evenly in the solution. The 19 states of the device conductance are modulated step by step by direct current scanning, which makes it possible to perform the computing task in memory. Then, the presence of conductive filaments allows the resistance state of the memristor to be dynamically transformed, which is similar to some basic behavior of biological synapses. Further, a variety of conductivity states such as short-term potentiation, short term depression, long term potentiation and long-term depression behavior are modulated by applying voltage to simulate the basic behavior of biological synapses. Finally, the paired pulse facilitation, paired pulse depression, spiking timing dependent plasticity and spiking voltage dependent plasticity behaviors of biological synapses are simulated in memristor devices.

High-Performance Memristive Synapse Composed of Ferroelectric ZnVO-Based Schottky Junction.

In pursuit of realizing neuromorphic computing devices, we demonstrated the high-performance synaptic functions on the top-to-bottom Au/ZnVO/Pt two-terminal ferroelectric Schottky junction (FSJ) device architecture. The active layer of ZnVO exhibited the ferroelectric characteristics because of the broken lattice-translational symmetry, arising from the incorporation of smaller V5+ ions into smaller Zn2+ host lattice sites. The fabricated FSJ devices displayed an asymmetric hysteresis behavior attributed to the ferroelectric polarization-dependent Schottky field-emission rate difference in between positive and negative bias voltage regions. Additionally, it was observed that the magnitude of the on-state current could be systematically controlled by changing either the amplitude or the width of the applied voltage pulses. Owing to these voltage pulse-tunable multi-state memory characteristics, the device revealed diverse synaptic functions such as short-term memory, dynamic range-tunable long-term memory, and versatile rules in spike time-dependent synaptic plasticity. For the pattern-recognition simulation, furthermore, more than 95% accuracy was recorded when using the optimized experimental device parameters. These findings suggest the ZnVO-based FSJ device holds significant promise for application in next-generation brain-inspired neuromorphic computing systems.

Investigation into Stress Relaxation and Creep Rate of C47200 Copper Alloy

Copper alloys have high thermal conductivity, relatively high mechanical strength, and toughness over a wide range of temperature; hence they are highly sorted for complex structural applications that required extreme heat flux under load. Creep of materials is classically associated with time-dependent plasticity under a constant stress/load at an elevated temperature, often greater than the absolute melting temperature. This research is aimed to study the evaluation of stress and creep rate in copper, identifying the mechanisms at which copper can easily be exposed to stress and creep deformations in structures. A 12 mm diameter copper rod with the composition of 52.05 % CuO and 30.26 % SnO2 was procured locally. Samples from the procured rod were heat treated to 650°C for 30 minutes and cooled in the still air as well as inside the furnace. Creep test was carried out at 760uC with a constant load corresponding to an initial stress (between 1.5 MPa and 350 MPa) and stress relation was carried out on a 98 kN capacity stress relaxation frame (from 350 MPa to 300 MPa). Rockwell hardness test and metallographic analysis (at 200 mm) were also conducted on the heat treated and unheated control samples. It was established that heat treatment reduced the hardness property of stress relaxed copper, accelerated the stress relaxation process up to 60 %, speed up both primary creep rate and the tertiary creep rate as well altered the linear creep pattern and behaviour.