Neuromorphic Speech Systems using Advanced ReRAM-based Synapse

S. Park1, A. Sheri1, J. Kim1, J. Noh1, J. Jang2, M. Jeon1, B. Lee1, B. R. Lee1, B.H. Lee1 and H. Hwang2,a)

1Gwangju Institute of Science and Technology (GIST), Gwangju, South Korea, 2Pohang University of Science and Technology, Pohang, South Korea

Phone: +82-54-279-2155, e-mail: hwanghs@postech.ac.kr

Abstract

We demonstrate an advanced ReRAM based analog artificial synapse for neuromorphic systems. Nitrogen doped TiN/PCMO based artificial synapse is proposed to improve the performance and reliability of the neuromorphic systems by using simple identical spikes. For the first time, we develop fully unsupervised learning with proposed analog synapses which is illustrated with the help of auditory and electroencephalography (EEG) applications.

I. Introduction Nowadays, biologically inspired analog computing systems, so-called neuromorphic systems, have been reported [1-2]. Specifically, ongoing efforts to realize artificial synapses are playing an important role in establishing such neuromorphic systems (Fig. 1). Emerging non-volatile memory technologies such as phase-change memory (PRAM), conductive-bridge memory (CBRAM) and oxide based memory (ReRAM) have been shown as good candidates for emulation of synaptic plasticity and learning rules like spike-timing dependent plasticity (STDP) as shown in Table 1 [1-7]. Compared to the local-filament type (Binary) synaptic device [1-5], interface switching type (Analog) synaptic device can be implemented as an artificial synapse without an external current compliance limit (set by outside resistor or transistor) [7].

II. Advanced synaptic ReRAM Technology

A. Programming scheme approach In our previous work, Al/Pr0.7Ca0.3MnO3(PCMO) devices were proposed to realize artificial synapses for emulating long term potentiation (LTP) and long term depression (LTD) (Fig. 3-5). However, it is difficult to emulate a gradual LTD using identical spikes due to such a large difference in the characteristics between SET and RESET operations. It can be explained by the different oxidation and reduction energies. Perovskite oxide such as PCMO can act as a reservoir of oxygen ions (O2−). When Al is deposited, the reaction between Al and PCMO can make a very thin AlOx layer. For the measurement,

dc bias was applied to the top electrode (TE) while the bottom electrode (BE) was grounded. When a negative bias is applied on the top electrode, O2- move from the AlOx layer to the PCMO bulk layer resulting the LRS. In contrast, a positive bias attracts O2- and forms a thick insulating oxide layer, which results in the HRS by preventing the electrons to conduct. As the electronegativity of the inserted Al is very low, it can attract oxygen easily. The oxide formation process should be much faster than the dissolution process. Such a conclusion is well supported by the asymmetric characteristics where set and reset processes have different pulse conditions. (Fig. 6(a)) [8,9]. To overcome these issues, we propose two approaches to improve the synaptic behavior; programming strategy and new materials. In this part, to implement the symmetric and gradual synaptic behavior, we used the programming schemes and obtained the advanced results as shown in Fig 6-7. Not only symmetric but also gradual responses (LTP, LTD) are the keys to improve the performance and reliability of the synaptic ReRAM device as shown in simulation results (Fig. 8).

B. Limitation of programming scheme approach Most recent realizations of ReRAM based synaptic device treat the synapse as a non-volatile multi-level resistor. Although such alternative methods (e.g., new architecture and programming scheme, as shown in Table 1) are desirable, there are limitations in terms of actual implementation. Programming schemes for multi-level operation in the ReRAM devices are complicated and it is extremely difficult to precisely control the local-filament. Gradual multi-level resistance modulation of ReRAM synapses may require a successive generation with non-identical neuron spikes (pulses with changing amplitude or width; or a combination of both), thus increasing the complexity of the peripheral CMOS neuron circuits which drive the synapses. The Incremental amplitude pulses lead to higher power dissipation and parasitic effects on large synaptic cross array devices. Therefore, in order to emulate spiking neural networks, it is necessary that synaptic

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potentiation/depression can be achieved by application of identical spikes. C. New material approach Material approaches are also proposed to improve the synaptic behavior without adopting the complicated programming schemes. We fabricated a 1k-bit synaptic ReRAM array using 8 inch wafer process. The system consists of a cross-point ReRAM array with device stack of Pt/AlOx/N-rich TiN/PCMO/Pt (from top to bottom) having active device area of 150×150 nm2 using a via-hole structure. As a top electrode (TE), 20 nm-thick TiN (N2 ambient), thin AlOx layer and 80 nm-thick Pt were subsequently deposited in a sequence and patterned (Fig. 9-10). By optimizing nitrogen concentration during TiN deposition and inserting AlOx layer, we observed the advanced synaptic behavior which results in the gradual increase and decrease of conductance states. Fig. 11 shows the analog memory characteristics in dc mode by using repeated incremental negative (①-⑥) and positive (⑦-⑬) current-voltage (I-V) sweeps. Fig. 12 shows experimental and simulation results of potentiation/depression characteristics as a function of the spike number. Thus, advanced synaptic characteristics with stable switching (Fig. 13-14) and high reliability are realized by using identical spike (Fig. 15).

III. Neuromorphic speech processing

: Auditory and EEG applications Pattern recognition techniques have brought new insights into where and how information is stored in the brain by prediction of the stimulus or state from the data. Neuromorphic speech processing is an emerging field attracting the attention of many researchers looking for the new paradigms helping in better understanding of the brain processes (Fig. 2). Vowels are important clues supporting speech recognition [10]. For extracting distinct EEG responses, selection of stimuli is one of the important factors for consideration. Thus, the three Korean vowels /a/, /i/ and /u/ were selected as our stimuli that they have a large difference in their formant frequencies (Fig. 16). In order to avoid the subject’s prediction of the following stimulus, stimuli /a/, /i/ and /u/ were randomly presented to the subjects. Fig.17 describes the processing flow of our neuromorphic speech system. Fig. 18 shows the classification of EEG responses to speech sounds. The EEG signals were obtained from eight electrodes in the right and left temporal areas during each of the three experimental conditions (speech /a/,

/i/ and /u/). As shown in the Figure 18, much of the alpha band (8-13 Hz) power in both right and left temporal areas is statistically different among three experimental conditions, and distinct responses over three phonemes can also be observed in the temporal area of topography. The brain response to speech /a/ shows high activation during 0-0.2 sec and 0.4-0.8 sec time intervals in the right and left temporal areas; the response to /i/ at 0.4-0.9 sec in the right and left temporal areas; and the response to /u/ at 0.2-0.4 sec in the right temporal and at 0.4-0.6 sec in the left temporal area (red rectangles). Fig. 19 shows the time and frequency domain results of three Korean vowels /a/,/i/, and /u/. The extracted signals from EEG and Cochlea are pre-processed by using emulator for conversion to input neuron [11]. Leaky Integrate and Fire (LIF) model were used for this simulation. The firing spikes of input 1000 excitatory neurons at cochlea layer are connected to 1st layer (400 neurons) randomly. The connections are 40% inhibitory chosen randomly and rest is excitatory. The processed data is then presented to a 1st layer feed-forward spiking neural network with 400 ReRAM synapses directly. The network is decided by winner takes all. The demonstrated applications exhibit high performance (prediction rate>90%).

IV. Conclusion The key elements to obtain advanced synaptic behavior of a high-speed neuromorphic system have been evaluated. For the first time, we demonstrated an auditory recall on an artificial neural network using EEG experiment. It could be applied in the development of brain-machine interfaces for restoring speech in paralyzed individuals.

Acknowledgments This research was supported by the Pioneer Research Center Program through the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning (2012-0009460).

Reference [1] M. Suri et al., IEDM Tech. Dig., p. 4.4, 2011. [2] S. Yu et al., IEDM Tech. Dig., p. 22.1, 2010. [3] S. Yu et al., IEDM Tech. Dig., p. 10.4, 2012. [4] D. Kuzum et al., IEDM Tech. Dig., p. 30.3, 2011. [5] M. Suri et al., IEDM Tech. Dig., p. 10.3, 2012. [6] A. Sheri et al., IEEE T. Industrial Electronics, accepted for publication, 2013. [7] S. Park et al., IEDM Tech. Dig., p. 10.2, 2012. [8] M. Hasan et al., Appl. Phys. Lett., vol. 92, no. 20, p. 202 102, May 2008. [9] D. Seong et al., IEEE Electron Device Lett., vol. 30, pp.919-921, 2009. [10] V. Cardin et al., Nature Communications 4, art. no. 1473, 2013. [11] Q.tan et al., J. Acoust. Soc. Am. 114 (4I), p. 2007, 2003

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Fig. 1 Schematic illustration of a neuromorphic device to emulate the biological neuron system.

Table 1. Comparison of neuromorphic researches focused on the synapse to improve synaptic behavior.

Fig. 2 Proposed auditory processing neuromorphic application.

Fig. 3 Typical I-V characteristics of Al/PCMO based 1k-bit synaptic ReRAM array. In set figure shows optical image of 1k-bit ReRAM array.

Fig. 4 Current change as a function of number of applied potentiation spikes. The values were -2 V, -2.5V, -3V,-3.5V, and -4V (read current at -1V).

Fig. 5 Current change as a function of number of applied depression spikes. The values were +1 V, +1.5V, +2V, +2.5V, and +3V (read current at -1V).

Fig. 6 Responding to programming spike (with potentiation and depression) by using various schemes as shown in Fig. 7. Fig. 6 (a) shows the abrupt nature of the reset transition in our devices. Precise control of the resistance was not possible during the pulsed reset process in Al/PCMO device due to large nonlinearity between positive and negative region and different speed of SET/RESET operations. To overcome these issues, we propose various programming schemes (Fig. 6(b) and 6(c)).

Fig. 7 Various programming pulse schemes. Gradual multi-level resistance modulation of RRAM synapses may require generation of successive non-identical neuron spikes.

Fig. 8 Simulation results of correlation between response shape and learning properties of RRAM based synapses. The parameter (P) was determined by conductance.

Fig. 9 The process flow of the cross-point 1k-bit synaptic RRAM array with AlOx/TiN/PCMO device.

Fig. 10 SEM image of the cross-point 1k-bit synaptic RRAM array. Inset shows the cross-points.

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Fig. 11 Repeated incremental negative and positive I–V sweeps. This graph shows the analog memory characteristics.

Fig. 12 result fowith AlOx/TiN

Fig. 13 Stable endurance characteristics of AlOx/TiN/PCMO device (Read current at +1V).

Fig. 14 AlOx/TiNcurrent a

Fig. 16 (a) EEG experiment (b) Formant frequencies of vowels. (/a/, /i/ and /u/)

Fig. 18 EEG eight electrodetemporal area.

Fig. 17 Concept and process flow of proposed system.

Fig. 21 Our sprocessing.

Experimental and simulation or potentiation and depression

identical spike of N/PCMO device.

Retention characteristics of N/PCMO device. (Read at +1V).

Fig. 15 Experimental resresistance modulation of Ridentical spikes (both amp

signals were obtained from es in the right and left

Fig. 19 Time and Frequedomain results of three Korvowels (/a/,/i/ and /u/).

spiking neural network simulated for Table 2. Recogni

sults: Successive gradual multi-level ReRAM synapses is obtained by using plitude and width were same).

ency rean

Fig. 20 Spiking neurons from cochlear simulation (neural network input).

tion testing results

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