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A Non-Volatile Optical Memory in Silicon Photonics
Jacqueline Geler-Kremer1,2, Felix Eltes1,3, Pascal Stark1, Ankita Sharma1, Daniele Caimi1, Bert Jan
Offrein1, Jean Fompeyrine1,3, Stefan Abel1,3
2
1
IBM Research − Europe, Säumerstrasse 4, 8803 Rüschlikon, Switzerland
Laboratory for Advances Materials Processing, Empa, Swiss Federal Laboratories for Materials and Technology, Feuerwerkerstrasse 39,
CH-3602, Thun, Switzerland.
3
Lumiphase AG, Dorfstrasse 147, 8802 Kilchberg, Switzerland
jgk@zurich.ibm.com
Abstract: We demonstrate a non-volatile optical memory element integrated in silicon
photonics for low-power reconfigurable photonic circuits and neural networks. Stable
transmission states are set by manipulating ferroelectric domains in BaTiO3 films embedded in
photonic waveguides. © 2021 The Author(s)
1. Introduction
The rise of artificial intelligence (AI) workloads, which are solved with large and computationally expensive
neural networks, demands for faster and more power efficient computing elements and architectures [1]. Optical
neural networks have been shown to be an excellent candidate to overcome the limitations of traditional digital
architectures [2–6]. However, none of them benefit from a true refractive optical synaptic element. Non-volatile
optical elements have been demonstrated using Ge2Sb2Te5 (GST), a phase change material (PCM), to modify the
optical transmission state in integrated waveguides [7,8]. However, PCM solutions rely on coupled changes of
both the real and imaginary part of the refractive index, which results in a change of not only the phase but also
the amplitude of the optical transmission. Our novel approach exploits the Pockels effect in BaTiO3 (BTO) and
shows a pure change of only the real part of the refractive index. We demonstrate unattenuated non-volatile phase
shifts in a photonic racetrack resonator made in a BTO-Si photonics platform. Our platform exploits a large
Pockels coefficient [9,10] in high quality BTO thin films which is compatible with photonic integrated circuits
(PIC) processes [11]. By manipulating the ferroelectric domains in a controlled way, we non-volatilely adjust the
magnitude of the effective Pockels coefficient to store the optical states.
2. Device Concept
The BTO-Si photonics platform uses Si waveguides on top of a BTO layer, allowing for the guided transverse
electric (TE) mode to be highly confined in the BTO layer while maintaining low optical losses. Due to the large
modal overlap with the BTO layer, the effective index of the mode scales with changes of the refractive index of
the BTO layer. Phase shifters comprise of a pair of electrodes along the sides of a BTO-Si waveguide. The
racetrack resonator device has phase shifters in two of the straight sections to induce variations of the refractive
index (Fig. 1a).
a.
c.
b.
Figure 1: a, Racetrack resonator device with BTO phase shifters. b, The position of the resonance in such resonator tracked as a function of
the applied DC voltage shows a hysteretic behavior due to ferroelectric domain switching. c, Different transmission spectra at a constant
bias after applying electrical pulses.
By changing the applied voltage, and thus the electric field, 𝐸, we directly change the refractive index in the
BTO layer, ∆𝑛!"# , as described by the Pockels effect,
$
*
∆𝑛!"# (𝐸) = − % 𝑟&'' (𝜈) 𝑛(,!"#
𝐸,
(1)
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where 𝑛(,!"# is the refractive index of BTO with no electric field present. 𝑟&'' is the effective Pockels coefficient,
a function of the fraction of domains aligned with the electric field, 𝜈. When slowly sweeping the applied DC
voltage, the resonance wavelength of the device changes with a hysteretic behavior (Fig. 1b), consistent with
ferroelectricity. At any non-zero bias, there is an opening of the hysteresis, corresponding to a window of
addressable ferroelectric domain states that match with a range of optical stable states, as shown by the red arrow
in Fig. 1b.
While maintaining a fixed bias voltage, electrical pulses are applied to switch between different ferroelectric
domain configurations, thus changing the real part of the refractive index of BTO and shifting the resonance of
the racetrack, as shown in transmission spectra of the device (Fig. 1c). Setting the states in the electrical domain
and reading them out in the optical domain represents a non-destructive readout mechanism. As multiple different
ferroelectric domain configurations are stable under a constant bias smaller than the coercive field, the optical
states of our device are non-volatile.
3. Device Characterization
The electrical pulses used to set the non-volatile optical states are defined by the amplitude of the pulses, their
width, as well as the number of pulses applied. We explored this parameter space in detail and demonstrated that
a plethora of states can be reached (Fig. 2).
We defined the “0” and “1” states as two extreme states that are set by applying 104 initialization pulses (300 ns
in width and ±12.1 V in amplitude). Each data point starts from the same initial condition, either the “0” or “1”
state, before applying the set pulse(s) followed by the optical measurement. The amplitude of the pulses has the
greatest impact on the state (Fig. 1a). For states previously set to “0” the largest increase can be seen between -5
to -10 V, after that the state change start to saturate. Numerous states can also be reached by applying multiple
pulses of the same amplitude (Fig. 2b). Using a closed loop setting scheme, we were able to set over 100 states
(Fig. 2c), not limited by the device itself but rather by the accuracy of our measurement equipment.
a.
b.
c.
Figure 2: a, Optical memory states as a function of applied pulse voltage, blue data points correspond to states when initialized at the state
“0”, and the pink data points correspond to states that have been initialized at “1”. b, Memory state as a function of number of pulses applied
for different voltages. c, Plot showing setting of over 100 distinct levels.
To implement our phase shifters in neuromorphic and programmable photonic applications, the memory
elements need to offer a large number of discrete states. At the same time, we must also be able to accurately and
quickly set the desired state that should be maintained over time.
By consecutively applying pulses of varying amplitudes, using an open loop setting scheme, we sequentially
reach distinguishable stable states (Fig. 3a). Eight stable states are shown by increasing the amplitude of the
pulses. To test the repeatability of reaching different states, we allow for setting of individual states in a random
pick and perform an optical readout after setting each state. The histogram (Fig. 3b) shows how we can accurately
distinguish between each desired state. Our device also shows great stability of the optical states for over 10
hours (Fig. 3c).
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a.
b.
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c.
Figure 3: a, Eight stable and distinguishable states reached by applying consecutive pulses of different amplitudes. b, Histogram of
randomly setting of the eight different states showing reproducibility of states. c, Long term stability of two different states.
4. Neuromorphic and Programmable Photonic Applications
Our BTO-Si photonic phase shifters can be used to build integrated non-volatile optical weights, a critical
component of optical neural networks and programmable photonic applications [6]. Our device concept combines
non-volatility, low power consumption and speed, powered by the use of the Pockels effect in ferroelectric BTO
to outperform current state of the art in photonic memory devices. The phase shifter we demonstrated could also
be embedded in a Mach-Zehnder interferometer as used to tune both the amplitude and phase of a signal. A nonvolatile optical memory element with these properties can broaden the possible architectures of photonic circuits.
5. Conclusion
We demonstrated a non-volatile optical phase shifter based on a BTO-Si technology. This new platform uses the
Pockels effect, a fast and efficient method to set different stable optical states. By using electrical pulses to
accurately switch between ferroelectric domain states, we are able reach a wide range of available levels. Such
phase shifters pave a path towards highly efficient photonic neural networks and novel programmable photonic
circuits.
6. Acknowledgements
This work received funding from the European Commission under grant agreement numbers H2020-ICT-20171-780997 (plaCMOS), H2020-ICT-2019-2-871330 (Neoteric), H2020-ICT-2019-2-871658 (Nebula), H2020ICT-2019-2- 871391 (PlasmoniAC). J.G.K. acknowledges support from the National Science Foundation under
grant no. IRES- 1358111 and financial support by Armasuisse Science and Technology.
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