(Invited) Analog Hfox-Rram Switches for Neural Networks
Authors: Spiga Sabina , Stefano Brivio , Covi Erika , Fanciulli Marco , Serb Alexander , Prodromakis Themis , Mostafa Hesham , Indiveri Giacomo 
Published: February 2020

Memristive systems represent today a disruptive technology for the semiconductor industry with high potential for a wide range of applications ranging from non-volatile memories and non-volatile logic, to analog circuits, biomimetic devices, and neuromorphic computing paradigms. Among the general class of memristive systems, our work focus on oxide filamentary resistive random access memories (oxide RRAM) that base their operation on redox reactions and electrochemical phenomena that allow the formation and dissolution of conductive filaments, shorting the two electrodes of a metal/oxide/metal stack [1]. RRAMs have already demonstrated their great potential for non-volatile memory applications because of low power consumption, fast switching times, scalability down to nm scale [2] or atomic level, and CMOS compatibility. In parallel, significant efforts to redirect the device engineering towards neuromorphic computing [3] and neural networks have been devoted by a large community in the last years. Indeed, the RRAM technology can localize memory and computation in one single device, thus allowing a conceptual realization of non-Von Neumann paradigms that dramatically reduce the power-cost in computing architectures and data communication. This research finds its main application in the development of intelligent and adaptive systems towards the ‘Internet of Things’ concept. It is a highly interdisciplinary research field which merges, on one hand, materials development, nanodevice fabrication, modelling, electrical testing aimed at building artificial synapses with memristive devices. On the other hand, it studies the implementation of neuron functions into VLSI electronics that has to match memristive behavior and electrical features for a functional and physical integration of artificial neuronal and synaptic units.
In our work we are focusing on switching dynamics in relation to material properties of HfOx-based RRAM devices [4-7] in order to reach the switching characteristics that fit biologically plausible behaviors. Here we present analog memristive behavior for use in two different approaches, named (i) 1 RRAM-1 synapse and (ii) 1T-1 RRAM–synapse for subthreshold CMOS circuits. In approach (i), a single RRAM device plays the role of a synapse by itself. The analog behavior allows realizing a Spike Timing Dependent-Plasticity (STDP) learning rule (Fig1a), which translates the timing of two superimposing spikes with specific shapes sent to the two device terminals into a causality relation among events [6-9]. The experimental STDP learning rule has been employed in a simulated neuromorphic network able to recognize vowels after an unsupervised training (Fig.1b).
While conceptually simple and allowing for a highly compact design, the previous approach is very challenging in practice. The 1T-1 RRAM synapse for subthreshold CMOS circuits, despite being less compact, is more feasible and allows some biological plausibility even within a network utilizing short simply shaped spikes. The use of subthreshold transistor in the analog regime refers back to the original definition of neuromorphic system as given by Carver Mead [9] and is used to generate the biorealistic temporal dynamics of post synaptic spikes [10]. We will demonstrate that RRAM devices can be interfaced with such synaptic circuits including subthreshold transistors to obtain a non-volatile control of synaptic weights.
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[2] J. Frascaroli , S. Brivio, F. F. Lupi , G. Seguini , L. Boarino , M. Perego, and S. Spiga , Resistive Switching in High-Density Nanodevices Fabricated by Block Copolymer Self-Assembly, ACS Nano 9, 2518 (2015)
[3] G. Indiveri, S.-C. Liu, Memory and information processing in neuromorphic systems, In Proceedings of the IEEE, IEEE, volume 103, 2015.
[4] J. Frascaroli, F. G. Volpe, S. Brivio, and S. Spiga, Effect of Al doping on the retention behavior of HfO2 resistive switching memories, Microelectronic Engineering 147, 104 (2015)
[5] S. Brivio, J. Frascaroli, and S. Spiga, Role of metal-oxide interfaces in the multiple resistance switching regimes of Pt/HfO2/TiN devices, Applied Physics Letters 107, 023504 (2015)
[6]E. Covi, S. Brivio, M. Fanciulli, and S. Spiga, Synaptic potentiation and depression in Al:HfO2-based memristor , Microelectronic Engineering 147, 41-44 (2015)
[7] S. Brivio, E. Covi, A. Serb, T. Prodromakis, M. Fanciulli, and S. Spiga, Gradual set dynamics in HfO2-based memristor driven by sub-threshold voltage pulses, in Memristive Syst. MEMRISYS 2015 IEEE Int. Conf. On (2015), pp. 1–2.
[8] E. Covi, S. Brivio, A. Serb, T. Prodromakis, M. Fanciulli, and S. Spiga, HfO2-based Memristors for Neuromorphic Applications, Proceedings of 2016 IEEE International Symposium on Circuits and Systems (ISCAS)
[9] C. Mead, Analog VLSI and neural systems. Reading, MA: Addison-Wesley (1989)
[10] Bartolozzi et al., Neural Computation 19 (2007) 2581.

Figure 1