Supplementary Materials for
Organic neuromorphic electronics for sensorimotor integration and learning in robotics
Imke Krauhausen, Dimitrios A. Koutsouras, Armantas Melianas, Scott T. Keene, Katharina Lieberth, Hadrien Ledanseur, Rajendar Sheelamanthula, Alexander Giovannitti,
Fabrizio Torricelli, Iain Mcculloch, Paul W. M. Blom, Alberto Salleo*, Yoeri van de Burgt*, Paschalis Gkoupidenis*
*Corresponding author. Email: [email protected] (A.S.); [email protected] (Y.v.d.B);
[email protected] (P.G.)
Published 10 December 2021, Sci. Adv. 7, eabl5068 (2021) DOI: 10.1126/sciadv.abl5068
The PDF file includes:
Supplementary Text Figs. S1 to S11
Legends for movies S1 and S2 References
Other Supplementary Material for this manuscript includes the following:
Movies S1 and S2
Supplementary Text 1. Line follower algorithm
The line follower algorithm is implemented using Lego Mindstorms® EV3 brick (Fig. 1B and S1) in combination with the Lego Mindstorms® Education software Version 1.4.2. The algorithm uses real-time raw data of reflectance sensor 1 (Fig. S1 and S2B) and the output voltage of the neuromorphic circuit (𝑉𝑀) as input. The memory state of the neuromorphic device influences its output 𝑉𝑀 and therefore also the outcome of the algorithm. Without the adaptivity of the neuromorphic circuit, the input 𝑉𝑀 of the line follower algorithm would be constant and there would be no change in the turn behavior of the robot.
The line follower algorithm consists of four action blocks that are executed sequentially, and in a loop (Fig. S2C+D). Starting on the line (on-track), the robot pushes to the left (by modifying the power distribution between the left and right servomotors) until it is driving off the line (off-track).
The background reflectance changes significantly; the next block initiates and the line follower swivels right to come back to the line. Upon reaching the line and hence detecting low reflection, the robot pushes to the right across the line. The reflectance changes again and the robot swivels left and comes back to the line again where the whole process starts over. As a result, the processes of line following has an oscillatory-like component due to the temporal scanning of the line with the reflectance sensor and the two servomotors.
In more detail, the turning behavior of the robot is controlled by two variables that are dependent on the motor voltage: SwivelValue and ReflectanceValue. SwivelValue influences the driving behavior by setting the steering direction. The steering direction obtains absolute values in the range of 0 - 100, where 0 means driving straight and 100 turning on the spot (i.e., rotation). In our case it varies in absolute terms between 50 and 90. The higher value equals the stronger swing, right turning is signed positive, left turning negative. ReflectanceValue is a threshold which is compared to the measured reflectance value of reflectance sensor 1, with values in the range of 20 and 75. The maze line has a reflectance of ~8-10 % and the background has a reflectance of ~80- 90 %. Navigation cues have a reflectance of 100% and are not distinguishable from the background for the algorithm because both values are below/above the possible thresholds, therefore have no effect on the line follower algorithm.
The algorithm determines in which input range (viz. output voltage of the neuromorphic circuit 𝑉𝑀), the two variables SwivelValue and ReflectanceValue change their values and define the transition region from one phase to the next (always left to always right turn). In the transition regime (150𝑚𝑉 ≤ 𝑉𝑀 ≤ 350𝑚𝑉) both values change with linear slope. Outside this regime (𝑉𝑀 ≤ 150𝑚𝑉 and 𝑉𝑀 ≥ 350𝑚𝑉) the values are constant.
The two variables can cause an imbalance between left and right actions. At a maze intersection, this implies that one direction can be favored (left or right). As an example, for 𝑉𝑀 < 150𝑚𝑉 (below the transition regime) this means (Fig. S2E): SwivelValue is 90 for swiveling right and -50 for swiveling left. ReflectanceValue needs to be above 20% for crossing the line to the left and above 75% for crossing to the right. Turning right is favored. For 𝑉𝑀 > 350𝑚𝑉 (above the transition regime), turning left is favored. By favoring left or right steering direction, the turn process at an intersection can be influenced strongly. Nevertheless, it is also dependent on the state of the loop (on-track or off-track).
2. Additional hardware unit
The additional hardware unit is used to tune and level sensor signals and to provide a supply voltage 𝑉𝑆𝑈𝑃𝑃 for the organic neuromorphic circuit. It preconditions the hardware signals passively without any adaptivity. The hardware unit receives the raw sensor data of reflectance sensor 2 and the touch sensor as input, and outputs the tuned and leveled sensor signals. A selector determines the polarity of the touch sensor signal, 𝑉𝐺,𝑀𝐸𝑀, in order to tune the memory device (MEM) in both directions (increase or decrease of the MEM conductance).
The signal of the reflectance sensor is adjusted by three potentiometers named Gain, Threshold, and Level (Fig. S2A). The potentiometer for Gain determines the sensitivity of reflectance sensor 2. The potentiometer for Threshold sets the detection threshold for forwarding the signal to the gate of the volatile device, 𝐺𝑂𝐸𝐶𝑇. If the sensor signal is below that threshold, no signal is passed on at 𝐺𝑂𝐸𝐶𝑇. With Level, its potentiometer levels the amplitude of output signal that is applied at 𝐺𝑂𝐸𝐶𝑇.
The Lego Mindstorms® EV3 brick provides a voltage of 5V which is downscaled to supply voltage 𝑉𝑆𝑈𝑃𝑃 = −0.5𝑉 for the organic neuromorphic circuit (Fig. S3B).
3. Transconductance change of OECT
The transconductance of the OECT that signifies the sensitivity, depends monotonically on its variable load resistor. In the case of the adaptive organic neuromorphic circuit, the resistor is replaced by an organic artificial synapse (MEM). The channel of the MEM device exhibits analogue modulation of conductance states. In the circuit configuration, these states can be described by a memory resistance 𝑅𝑀𝐸𝑀. The behavior of the OECT can be described by the Bernards-Malliaras model which uses a two-circuit approach as representation of the electronic and ionic charge transport phenomena (42). Considering the neuromorphic device as resistor 𝑅𝑀𝐸𝑀, the neuromorphic circuit can be described as voltage divider with two resistors (Fig. 2A), 𝑅𝑀𝐸𝑀 and 𝑅𝑂𝐸𝐶𝑇, with 𝑅𝑂𝐸𝐶𝑇 the channel resistance of the volatile device (OECT). The voltage partition 𝑉𝑀, is given by:
𝑉𝑀 = 𝑅𝑀𝐸𝑀
𝑅𝑀𝐸𝑀+ 𝑅𝑂𝐸𝐶𝑇𝑉𝑆𝑈𝑃𝑃
With 𝑉𝑆𝑈𝑃𝑃, the supply voltage of the neuromorphic circuit. The transconductance of the OECT in the saturation regime of the transistor is given by (42):
|𝑔𝑚| = 𝜕𝐼𝐷,𝑂𝐸𝐶𝑇
𝜕𝑉𝐺,𝑂𝐸𝐶𝑇 = 𝜇𝑊𝑇𝐶𝑐ℎ′
𝐿 𝑉𝑀 =𝜇𝑊𝑇𝐶𝑐ℎ′ 𝐿
1 1 +𝑅𝑂𝐸𝐶𝑇
𝑅𝑀𝐸𝑀 𝑉𝑆𝑈𝑃𝑃
where 𝜇 the holes mobility of p(g2T-TT), 𝐿 the length, 𝑊 the width and 𝑇 the thickness and of the channel. 𝐶𝑐ℎ′ is the volumetric capacitance of p(g2T-TT). The above equation serves as the basis of the simulation shown in Fig. 2C.
The following parameters are used for the simulations: 𝐿 = 480𝜇𝑚, 𝑊 = 80𝜇𝑚, 𝑇 = 40𝑛𝑚, 𝐶𝑐ℎ′= 241 𝐹
𝑐𝑚3, 𝜇 = 0.94𝑐𝑚2
𝑉𝑠 . Physical device dimensions (𝐿, 𝑊, 𝑇) are defined by the lithographic process and via profilometry. Materials related parameters are previously reported (43). A supply voltage 𝑉𝑆𝑈𝑃𝑃 = −0.5𝑉 is used, and resistance ratios 𝑅𝑀𝐸𝑀/𝑅𝑀𝐸𝑀 = 1 − 100 are simulated.
4. Probability experiments for turn behavior
The non-deterministic nature of the turn behavior via the static line follower algorithm is analyzed the with the help of a dummy circuit. The dummy circuit, a potentiometer that simulated the change of 𝑉𝑀 was used for test turns at a maze intersection with a sample size of 𝑁 = 50 trials per voltage.
The results of these experiments with the full sample size are presented in Fig. S10, the average is also integrated in the graphs on the probability of turn (Fig. 2E and 3B).
5. Object tracker
To extract the paths of the robot from video data, an object tracker was implemented in Python using the open-source package OpenCV. OpenCV version 4.4.0 and Python version 3.8.5 are used.
The object tracker uses the tracker type 'boosting'. It automatically finds a marker on the robot by means of its color and tracks its trajectory throughout the training process. The path trajectories of the individual training steps are then shown in the final output image.
Fig. S1. Robotic setup. The digital control unit of the robot, the Lego Mindstorms EV3 brick, is marked with a blue box. The robotic hardware consisting of three sensors, an additional hardware circuit and the neuromorphic circuit is marked in red. The three sensors are: the reflectance sensor 1 as input for the line follower algorithm (supplementary text and Fig. S2), the reflectance sensor 2 to detect navigation cues (signal 𝑉𝐺,𝑂𝐸𝐶𝑇), and the touch sensor used to train the MEM device (signal 𝑉𝐺,𝑀𝐸𝑀) of the neuromorphic circuit. The robot drives on two wheels, each controlled by a separate servomotor. The maze setup is constructed out of printed canvas. The beige-grey background of the maze is clearly distinguishable from the highly reflecting white navigation cues and the non-reflecting black track lines. The maze tracks are in a honeycomb-like pattern consisting of hexagonal unit cells. Photo Credit: Imke Krauhausen, Max Planck Institute for Polymer Research.
Fig. S2. Line follower algorithm. (A) Complete robotic setup. (B) Reflectance sensor 1 used as input line follower algorithm, marked by red box. (C) Four-step moving pattern of the robot implemented by the line follower algorithm. (D) Generalized four-step algorithm for line following in form of a flow chart. (E) Detailed four-step algorithm for line following in form of a flow chart.
SwivelValue and ReflectanceValue that are variables depending on the input voltage 𝑉𝑀 (output of the neuromorphic device) of the algorithm. By influencing the SwivelValue and ReflectanceValue variables, the turn behavior can be favored to the left or right. Photo Credit: Imke Krauhausen, Max Planck Institute for Polymer Research.
Fig. S3. Analog hardware unit for conditioning. (A) Circuit diagram of the additional analog hardware unit of the robotic setup. The potentiometers (Gain, Threshold and Level) allow for passive adjustment of the signal passed on by reflectance sensor 2. Gain determines the sensitivity of the sensor. The signal is only passed on of it is above the set Threshold. The last potentiometer, Level, adjusts the signal level of the sensor. (B) The Lego Mindstorms brick provides a 5V output voltage which is downscaled to a supply voltage 𝑉𝑆𝑈𝑃𝑃 = −0.5𝑉 for the organic neuromorphic circuit.
Fig. S4. Layout of the organic neuromorphic circuit. (A) Mask layout for the organic neuromorphic circuit. Each glass slides fits four circuits; one circuit is marked with a red box. (B) Photograph of an exemplary glass slide with four organic neuromorphic circuits. The ionic gel is visible as dried drops on top of the channel and gate areas. Photo Credit: Imke Krauhausen, Max Planck Institute for Polymer Research.
Fig. S5. Volatile characteristics of the OECT device. (A) Representative transfer characteristic (𝐼𝐷− 𝑉𝐺,𝑂𝐸𝐶𝑇) of an OECT device for cyclic 𝑉𝐺,𝑂𝐸𝐶𝑇 sweep (rate 0.5V/s). The device shows hysteresis-free characteristic indicating volatile behavior. (B) Transient characteristics of the OECT (𝑉𝐷− 𝑇𝑖𝑚𝑒) when connected with a variable load resistor 𝑅𝐿𝑂𝐴𝐷. The OECT shows no offset in the drain voltage 𝑉𝐷, when a voltage pulse is applied at the gate 𝑉𝐺,𝑂𝐸𝐶𝑇 highlighting the volatile nature of the OECT device.
Fig. S6. Response time of the OECT device for sensory inputs. (A) Via line follower process, the line of the maze is scanned with the reflectance sensor. (B) Time-varying signal 𝑉𝐺,𝑂𝐸𝐶𝑇 as the line of the maze is scanned with the reflectance sensor. (C) Representative example of the scanning time 𝛥𝑡 (mean < 𝛥𝑡 > and minimum 𝛥𝑡𝑀𝐼𝑁 scan times are indicated in the graph). (D) Transient behaviour of the output voltage 𝑉𝑀 of the neuromorphic circuit and its corresponding time response 𝜏. The response of the neuromorphic circuit is fast enough to follow the line scanning process (𝜏 <
< 𝛥𝑡). Photo Credit: Imke Krauhausen, Max Planck Institute for Polymer Research.
Fig. S7. Analogue memory characteristics of the MEM device. Tuning of the channel resistance RMEM of the MEM device for 𝑉𝐺,𝑀𝐸𝑀 = −500𝑚𝑉, 𝑡𝑀𝐸𝑀~0.5𝑠 (here shown as the corresponding gate current 𝐼𝐺,𝑀𝐸𝑀). For the specific probing conditions (i.e., input voltage, time), the channel resistance RMEM exhibits ~50 states.
Fig. S8. Modeling of the neuromorphic circuit. (A) Modeling of the OECT output and transfer characteristics. The measurements are depicted as triangle markers. (B) Modeling of the resistance
states of the memory device. The measurements are shown in blue. (C) Modeling of the combined circuit of OECT and memory device during the application of gate pulse at the OECT. The measurements are outlined with triangle markers. The black solid curve represents the model in all panels. The model-based circuit simulations are able to reproduce and predict the measurements accurately. The device parameters are the following. WOECT = 80 m, LOECT = 480 m, and TOECT
= 40 nm is the OECT channel width, length and thickness, respectively. WMEM = 80 m, LMEM = 240 m, and TMEM = 40 nm is the MEM channel width, length and thickness, respectively.
CCH,OECT = CV·WOECT·LOECT·TOECT is the channel capacitance of the OECT device and CCH,MEM = CV·WMEM·LMEM·TMEM is the channel capacitance of the MEM device. RSSE is the resistance of the solid-state electrolyte due to the ionic transport from the gate to the channel. CG,OECT = 38.4 10-6 F and CG,MEM = 192 10-9 F is the gate capacitance of the OECT and MEM device, respectively. RSENS
= 100 MΩ is the series resistance of the robot touch sensor.
Fig. S9. Output of the organic neuromorphic circuit and training evolution. During training 𝑉𝑀 changes in two ways: Firstly, the baseline of 𝑉𝑀 rises by applying a gate voltage at the non- volatile device (MEM) changing its state. The deviation from the initial setting is depicted with circled markers. Each step leads to an increase in the baseline of 𝑉𝑀 (total 𝛥𝑉𝑀 = 0 − 80𝑚𝑉 with a step change of ~6𝑚𝑉). Secondly, the sensitivity of the volatile device (OECT) is enhanced when its drain voltage is increasing. Thus, the peak of 𝑉𝑀 which occurs while registering a navigation cue also increases. The deviation from the initial setting is depicted with triangle markers. The combination of both surges in voltage leads to a significant change from the initial setting and ultimately to the change in turning behavior.
Fig. S10. Turn behavior of the robot as modulated by the output voltage of the neuromorphic circuit. Turn behavior of the robot as a function of the voltage 𝑉𝑀 using a dummy circuit to emulate 𝑉𝑀. Sample size for each voltage step is N=50 trials in a maze intersection.
Fig. S11. Training evolution as a function of cue detection and path completion. (A) Mean number of navigation cues for each training step 𝑁. The path consists of nine cues. (B) Mean percentage of path completion for each training step. The turn behavior is a non-deterministic process; multiple runs are completed for different training steps (5-8 runs in the maze for each training step). The mean of the runs and the standard deviation is shown in the graphs.
Movie S1.
Training evolution for target path 1.
Movie S2.
Navigation in target path 2, after the formation of the visuomotor association.
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