Robotic platform for film deposition, annealing, and imaging

Deposition, annealing, and darkfield imaging of all the organic thin films included in the database were performed using a flexible robotic platform configured for thin-film experiments described in detail in ref. 30. Briefly, the robotic platform consists of a multi-purpose robotic arm that can handle fluids and planar glass substrates, as well as a variety of other modules that enable other tasks to be performed. The modules relevant to this study include: trays of stock solutions and mixing vials which enable the formulation of spin-coating inks with various compositions; a spin-coater for depositing inks on substrates to form thin films; an annealing station for variable-time annealing of thin films; a darkfield imaging station for imaging the thin films.


Toluene (ACS grade) was purchased from Fisher Chemical, and was used without further purification. Acetonitrile (≥99.9%), 2-propanol (≥99.5%), acetone (≥99.5%), 4-tert-Butylpyridine (96%), Spiro-MeOTAD (99%), FK 102 Co(III) TFSI salt (98%, SKU 805203-5G), and Zinc di[bis(trifluoromethylsulfonyl)imide] (Zn(TFSI)2, 95%) were purchased from Sigma Aldrich and were used without any further purification. Extran 300 Detergent was purchased from Millipore Corporation. Titanium(IV) 2-ethylhexanoate (97%) was purchased from Alfa Aesar and was used without any further purification. White glass microscope slides (3″ × 1″ × 1 mm) were purchased from VWR International. Fused silica wafers (100 mm diameter, 500 µm thickness, double-side polished) were purchased from University Wafer.

Fused silica wafers and microscope slides were cleaned prior to thin film deposition. A solution of 1% v/v Extran 300 in deionized water was prepared. The substrates were sonicated successively in the diluted Extran 300, deionized water, acetone, and 2-propanol. Before each sonication step, the substrates were rinsed in the following solvent. Substrates were stored submersed in 2-propanol. Prior to use, the substrates were dried with filtered, compressed air and inspected by eye for defects.

Organic thin film deposition

Stock solutions of spiro-OMeTAD, FK102 Co(III) TFSI salt, Zn(TFSI)2, and 4-tert-butylpyridine were prepared at 50 mg mL−1 in 1:1 v/v acetonitrile/toluene. These stock solutions were combined using the robotic platform described above to form 150 µL of ink. 100 µL of ink was deposited by the robotic platform onto a microscope substrate rotating at 1000 rpm; rotation was maintained for 60 s following ink injection. The resulting thin films were then annealed for 0 to 250 s using a custom forced air annealer (an aluminum enclosure around heat gun, Model 750 MHT Products, Inc.). All of these procedures are described in more detail in ref. 30.

Metal oxide thin film deposition

Amorphous titanium oxide films were prepared by manual spincoating. The samples were prepared by pipetting 100 µL of Titanium(IV) 2-ethylhexanoate solution (0.1 M, 2-propanol) onto cleaned fused silica wafers rotating at 3000 rpm; rotation was maintained for 30 s following ink injection. The resulting samples were irradiated with deep ultraviolet light (Atlantic Ultraviolet G18T5VH/U lamp – 5.8 W 185/254 nm, ~2 cm from the bulb, atmospheric conditions) for 15 min. After irradiation, the samples were transparent and highly refractive.

Robotic darkfield imaging

All darkfield images taken with the robot were captured with a FLIR Blackfly S USB3 (BFS-U3-120S4C-CS) camera using a Sony 12.00 MP CMOS sensor (IMX226) and an Edmund Optics 25 mm C Series Fixed Focal Length Imaging Lens (#59-871). The C-mount lens was connected to the CS-mount camera using a Thorlabs CS- to C-Mount Extension Adapter, 1.00”-32 Threaded, 5 mm Length (CML05). The sample was illuminated from the direction of the camera using an AmScope LED-64-ZK ring light. For imaging, the lens was opened to f/1.4, and black flocking paper (Thorlabs BFP1) was placed 10 cm behind the sample.

Bright-field microscopy

All brightfield images were collected using an OLYMPUS LEXT OLS 3100 microscope operating in bright-field reflection mode using ×5 and ×20 objectives.

Monotonic dewetting experiment

To collect images of an organic thin film monotonically dewetting over time, a thin film of Spiro-OMeTAD and FK102 Co(III) TFSI salt was deposited (but not annealed) using the robotic platform as described above. A camera and lightsource were positioned above the sample in the same way as they were for the robotic darkfield imaging setup. A heat gun (Model 2363333, Wagner) was positioned to heat the sample from below at a 45° degree angle so as not to obscure the black background from the camera. To perform the experiment, the heat gun was turned on high and images were acquired every second for 100 s.

Image labeling procedure to define ground truth for model development

The extent of dewetting in the dark-field images was scored by up to 3 experts on an integer scale from 0 to 9. The extent of cracking in these images was, separately, scored in the same way. In both cases, the average of the available scores was used as the ground truth. All experts used the same graphical user interface to perform the labeling. The darkfield images and the associated scores, as well as the labeling GUI can be found online (see “Data availability” section)

For the brightfield images, a vector of binary values was assigned by a single researcher to each image. Each element of the vector indicated the presence or absence of one type of defect from the following: cracks, dewetting, particles, scratches, non-uniformities. In this way, images could be labeled as having no defects, one defect (of a specified type) or more than one defect (with the types present specified). The brightfield images and the associated labels are also available online (see “Data availability” section).

Development of the DeepThin network

The DeepThin CNN architecture (Fig. 1) was developed for the thin-film image analysis tasks described here and is inspired by the VGG16 CNN architecture36. Initially, DeepThin was trained using only one convolutional layer. The model complexity was iteratively increased until the model accuracy stopped improving. We employed five-fold cross-validation37 to find a high-performance model before evaluating the model on the unseen validation data.

The input layer to DeepThin is an image with 3 RGB color channels. DeepThin has several convolutional and pooling layers as detailed in Fig. 1. The first convolutional layer uses 32 filters with a 3 × 3 × 3 kernel to convolve over the image, creating an output of size 50 × 50 × 32. Zero padding is performed so that the resulting image size is identical to the input image size. The output of the convolutional layer is passed into a ReLU activation layer. This convolutional layer is repeated, as in the VGG16 model.

Next, a maximum pooling layer of kernel size 2 × 2 is convolved over the output of the previous layer to generate a 25 × 25 × 32 output, returning the maximum value for a kernel. The two convolution layers and the pooling layer are repeated a second time. The output of the second maximum pooling layer is flattened to a 2000 × 1 vector. This output is followed by two fully connected layers of 20 neurons with ReLU activation functions and a final layer that outputs defects classes by applying a sigmoid activation function. DeepThin is trained by minimizing an error function through backpropagation using the stochastic gradient descent method. L2 (Gaussian) and Dropout regularization was used to reduce interdependent learning amongst the neurons. Regularization reduces overfitting by adding a penalty to the loss function.

DeepThin was trained using the Adam optimizer38, with an initial learning rate of 0.001 and a batch size of 100. Training loss and validation loss converged after 11 epochs.

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