The search for new “neuromorphic computing” architectures that mimic the brain’s approach to simultaneous processing and storage of information is intense. Because, in real brains, neuronal synapses outnumber neurons by many orders of magnitude, the realization of hardware devices mimicking the functionality of a synapse is a first and essential step in such a search. We report the development of such a hardware synapse, implemented entirely in the optical domain via a photonic integrated-circuit approach. Using purely optical means brings the benefits of ultrafast operation speed, virtually unlimited bandwidth, and no electrical interconnect power losses. Our synapse uses phase-change materials combined with integrated silicon nitride waveguides. Crucially, we can randomly set the synaptic weight simply by varying the number of optical pulses sent down the waveguide, delivering an incredibly simple yet powerful approach that heralds systems with a continuously variable synaptic plasticity resembling the true analog nature of biological synapses.

Several electronic devices have recently been investigated to achieve synaptic function, such as those based on electrically induced resistive changes in phase-change chalcogenides ( 11 – 13 ), metal-insulator-metal structures ( 14 , 15 ), and ferroelectric materials ( 16 ), as well as nanomaterial-based field-effect transistors ( 17 , 18 ). A photonic synapse based on microfibers ( 19 ) and an optoelectronic synapse using carbon nanotubes ( 20 ) have also been demonstrated with potential benefits of large bandwidth ( 21 , 22 ) and no electrical interconnect power loss ( 22 ) inherently from the computing by optical means, although they tend to be either difficult to integrate and speed-limited, or still rely on electrical excitation signals. Here, we demonstrate a fully integrated all-photonic synapse based on phase-change materials (PCMs) that resembles the neural synapse at the physical level and can achieve synaptic plasticity compatible with the well-known Hebbian learning or spike timing–dependent plasticity (STDP) rule.

( A ) Structure of neuron and synapse. Inset: Illustration of the synapse junction. ( B ) Schematic of the integrated photonic synapse resembling the function of the neural synapse. The synapse is based on a tapered waveguide (dark blue) with discrete PCM islands on top, optically connecting the presynaptic (pre-neuron) and the postsynaptic (post-neuron) signals. The red open circle is a circulator with port 2 and port 3 connecting the synapse and the post-neuron; weighting pulses are applied through port 1 to the synapse. ( C ) Optical microscope image of a device with the active region (red box) as the photonic synapse. The optical input to and output from the device are via apodized diffraction couplers (white boxes). Inset: A typical photonic chip containing 70 photonic synapses has a dimension smaller than a 5-pence coin. ( D ) Scanning electron microscope image of the active region of the photonic synapse corresponding to the red box in (C) with six GST units (1 μm × 3 μm, yellow, false-colored) on top of the waveguide (blue, false-colored). Inset: The zoomed-in tapered structure of the waveguide highlighted by the white dashed box.

In stark contrast to conventional computing systems based on the von Neumann architecture where the central computing unit is separated from the main memory, the human brain contains a large number of neurons with synapses, each of them acting as both the computing and the memory unit ( 1 ). This unique structure makes the brain energy-efficient ( 2 ) in dealing with emotions, learning, and thinking—actions that are nearly impossible, or at least far less efficient and effective, for traditional computers ( 3 ). As shown in Fig. 1A , a neuron (pre-neuron) generates action potentials (spikes, fire time t pre ) that propagate along the axon and are transmitted through a junction to the next neuron (post-neuron) that generates the postsynaptic action potentials (fire time t post ). The junction is called a synapse (inset in Fig. 1A ), with the synaptic weight (w) determining the communication strength between the two neurons. The synaptic plasticity (that is, the change in synaptic weight) Δw is determined by neural activities, for example, Δw = f(t post − t pre ) based on the Hebbian learning rule ( 4 ), and is believed to be the primary mechanism for memory and learning in the human and animal brain ( 5 ). Inspired by the brain, neuromorphic computing that attempts to imitate the neural system at the physical level has gained significant attention, recently driven by the needs of “big data” ( 6 ), artificial intelligence ( 7 ), and a supporting computing concept for the internet of things ( 8 ). As a first and key step to the construction of neuromorphic architecture, it is essential to develop suitably plastic synapse-like devices ( 1 , 9 , 10 )—not least because synapses are by far the most numerous component of real brains, outnumbering neurons by several orders of magnitude ( 2 ).

RESULTS

Concept of on-chip photonic synapse The concept of a photonic synapse is shown schematically in Fig. 1B. A waveguide with discrete PCM structures on top acts as the photonic synapse with the input and output of the waveguide connected with a pre-neuron and a post-neuron. An optical circulator is used for connecting the output of the synapse and the post-neuron (from port 2 to port 3) and for applying optical pulses to alter the synaptic weight (from port 1 to port 2). Low-energy optical transmission can be measured from the pre-neuron to the post-neuron, with the transmission level dependent on the synaptic weight. It has previously been demonstrated that optical pulses can switch PCMs integrated on waveguides to provide non-volatile photonic memories storing up to eight levels in a single cell (23). To move between levels in that memory implementation required single pulses with varying powers for amorphization and a complicated multipulse, multipower format for recrystallization. However, for the realization of a practicable synapse mimic, not only precise control of synaptic weighting but also a device whose weight is controlled by fixed pulse characteristics is crucial. Here, we achieve this by designing a tapered waveguide structure and then incorporating multiple, small, discrete PCM islands (Fig. 1, C and D). This enabled us to have much improved control of the electric field (of the optical pulses propagating along the waveguide) that interacts with the PCM. This resulted in a very effective method for synaptic weight control that is based entirely on the number of fixed-duration, fixed-power excitation pulses applied, as we demonstrate later.

Synaptic-mimic design with FEM analysis We use the well-studied chalcogenide Ge 2 Sb 2 Te 5 (GST) (24–27) for the PCM cells, with indium tin oxide (ITO) as the capping layer (see Materials and Methods). Figure 1C shows the optical image of a typical device. Two diffraction grating couplers are used to connect the device with fiber arrays for signal transmission (Fig. 1C and fig. S1). The central part of the waveguide (with the discrete GST islands on top) is tapered to a 0.8-μm width from a nominal width of 1.3 μm elsewhere (Fig. 1D). The effectiveness of the combination of the tapered structure with the discrete PCM islands for controlling the interaction field along the waveguide is exemplified in Fig. 2 (and fig. S3). Here, a transverse electric (TE) optical field at 1580 nm is “launched” into the left side of several alternative waveguide designs (Fig. 2, A and B, and fig. S3, A and B), and the strength of the electric field (E-field) at the waveguide surface is simulated for the PCM in the crystalline (Fig. 2 and fig. S3) and amorphous phase (fig. S3). With the GST in the crystalline phase, a simple rectangular waveguide with a single large GST cell on top [Fig. 2A, as used in the memory application of Ríos et al. (23)], the E-field decays rapidly because of the very strong absorption and exhibits many resonant peaks and troughs (standard design in Fig. 2C). However, for the tapered waveguide with discrete GST islands (synapse-mimic design in Fig. 2C), the interaction of the E-field (and so the absorption) is much more “controlled,” with a much more gradual decay and little evidence of resonance effects. To quantitatively compare the electric field distributions in the various structures, we calculated the average, SD, and range of the E-field inside the GST regions, as well as the ratio between the fields at the right ( E GST out ) and left sides ( E GST in ) of the GST cells (Fig. 2D). The SD and the range of the electric field in the GST regions of the synapse-mimic structure are the smallest, and this structure also has the highest E GST out / E GST in ratio, thus demonstrating the smoothest distribution of the E-field inside and that most energy transmitted past the GST cells in this structure (detailed comparisons between four structures are elucidated in section S2 of Supplementary Text). Fig. 2 Finite element method (FEM) simulations of the photonic synapse with different structures. (A) Top: Schematic shows photonic synapse with a standard design: a straight waveguide with a thin film of GST (6 μm × 0.8 μm, orange block) on top. Bottom: TE mode E-field distribution at the surface of the waveguide with the entire GST film (white box) in the crystalline state. (B) Top: Schematic shows photonic synapse with synapse-mimic design: a tapered waveguide with six discrete GST islands (1 μm × 0.8 μm each) on top, which is the structure used in our experiments. Bottom: E-field distribution with all GST islands in crystalline states. (C) E-field distributions along the center line of the waveguide surface. The yellow, purple, green, and cyan curves correspond to the E-field distribution along the dashed horizontal lines in (A) and (B) (standard and synapse-mimic) and fig. S3 (A and B) (S1 and S2), respectively. The dashed red (black) lines illustrate the left and right boundaries of the GST film (discrete GST islands) in standard and S1 (synapse-mimic and S2) designs. (D) Top: Statistical results of the E-field inside the GST film or islands (square, average value; box, SD; bottom and top lines, minimum and maximum values). Bottom: The ratio between the average E-field at the output ( E GST out ) and input ( E GST in ) edges of the GST film or islands corresponding to the orange dashed lines in (A) and (B) and fig. S3 (A and B).

Synaptic weighting and plasticity The enhanced E-field control engendered by the use of the structure comprising a tapered waveguide with discrete PCM islands leads directly to the implementation of a simple yet most effective integrated photonic synapse, as we now show. Before optical measurements, the devices were annealed on a hot plate (~250°C) for 10 min to completely crystallize the GST. The optical transmission (T 0 ) of the device with the GST in the fully crystalline state is defined as the baseline of the readout and assigned to a synaptic weight “0.” Any subsequent change of the readout (ΔT = T − T 0 ) during the measurement is normalized as the relative change in percentage (ΔT/T 0 ) to the baseline (Fig. 3A). Changes in synaptic weighting, as shown in Fig. 3, were then achieved by sending fixed-duration, fixed-energy optical pulses down the waveguide. Using one single pulse of 50 ns at 243 pJ (section S1 of Supplementary Text), the transmission readout changed by ~7% to weight “3” (arbitrarily defined, but predetermined weight numbers). The weight could then be decreased from weight “3” to “1” with 100 identical pulses (repetition rate, 1 MHz; total weighting time, 100 μs) using the exact same pulse parameters (that is, 50 ns, 243 pJ). When we increased the pulse number to 1000 (total weighting time, 1 ms), the device went from weight “1” to “0.” This represents a crucial advance, because it allows one to arbitrarily adjust the synaptic weight using a set of known pulses without having previous knowledge of the current actual weight. Fig. 3 Synaptic weighting and plasticity. (A) Demonstration of differential synaptic weighting of the device in Fig. 1 during switching between the crystalline and amorphous states of GST islands with the relative transmission change (ΔT/T 0 ) recorded. Each weight can be reached with the same number of pulses (50 ns at 243 pJ, 1 MHz) from any previous weight. (B) Repeatability of the weighting over multiple cycles. Inset: Statistical analysis of the change in readout for weight “0,” “1,” and “4.” The pulse applied here was 50 ns at 320 pJ, slightly larger than that in (A). (C) Five weights of the photonic synapse are obtained with switching at optical pulse energy (404.5 pJ, 50 ns). The dashed blue (yellow) boxes correspond to the first (last) weighting cycle. The upward and downward arrows in the boxes are the weighting directions. (D) Photonic synaptic weight (ΔT/T 0 ) as a function of optical pulse numbers. The left (right) panel corresponds to the data of the dashed blue (yellow) box in (C). The solid triangles (hollow boxes) are the data from the upward (downward) weighting direction. The dashed lines are the exponential fittings of the data with detailed fitting parameters elucidated in table S2. This finding is worth discussing further. For example, weight “1” in Fig. 3A can be switched from weight “3,” “2,” or “0” with exactly the same parameters of pulses (100 pulses of 243 pJ and 50-ns length); similarly, weight “0,” “2,” and “3” can be set with 1000, 50, and 1 pulses, respectively, from any (undetermined) previous weight. Moreover, we examined the long-term durability of the switching between different weights, as shown for example in Fig. 3B. Even after 38 cycles of switching, carried out over a period of many minutes, the individual weights are clearly distinguishable with the deviation of each weight below 0.77% in readout transmission. These results illustrate a direct and unambiguous achievement of deterministic synaptic weighting using a known number of optical pulses (section S3 of Supplementary Text). To uncover the relationship between the synaptic weight and the pulse number in more detail, we increased the power of the pulse to obtain five stable weights with high signal-to-noise ratio (Fig. 3C and fig. S5). The device started at weight “0” (baseline), which was then followed by pulses of 200, 100, 50, and 1 (404.5 pJ, 50 ns) to reach weights “1,” “2,” “3,” and “4,” respectively; the pulse sequence was then reversed (that is, 50, 100, 200, and 1000 pulses applied corresponding to a total weighting update time of 50 μs, 100 μs, 200 μs, and 1 ms, respectively) to access weights “3,” “2,” “1,” and “0,” and the whole process was repeated 10 times. We extracted the mean value of the transmission change for each weight (monitored for ~10 s) from the 1st and 10th weighting cycle and plotted them versus the corresponding pulse numbers, shown in Fig. 3D. First, as previously stated, we see that the change in synaptic weight is exponentially and monotonically dependent on the number of pulses applied. This relation is very stable and is sustained for long periods of time (it took us 7 min to complete the experiments in Fig. 3C and fig. S5, for example). Moreover, we note that, by tuning of the pulse parameters, we can readily achieve an increased number of synaptic weights. For example, with a decreased pulse width of 20 ns and a pulse energy of 216 pJ, we readily achieved 11 synaptic weights, with a similar exponential relationship between the change of synaptic weight and the number of pulses (fig. S6). By further pulse control, and/or by increasing the signal-to-noise ratio [for example, by increasing the readout power (23)] and/or by further device optimization, it will be possible to access considerably more synaptic weighting levels or even achieve continuous weighting to mimic the true analog nature of synaptic weight change in biological systems (28).