Integrated circuit (IC) chips form the backbone of the electronics industry; and like any other high-power industry, component manufacturers are fluid, constantly changing, dividing, and merging ownerships. For this reason, successful IC startups or spinoffs will often have IC components that are still sold and used on modern circuit boards despite the company itself not having a large online presence. Needless to say, this can make tracking down individual components difficult.
Logos
IC components are small. Many of the most popular components (resistors, capacitors, etc.) rarely have manufacturer information. Even the most basic information, like company logos or part numbers can be missing. Even when the part numbers are present, it may be difficult to track down which manufacturer created and sold the part, making part replacement nearly impossible.
Logos for each brand are double-checked on various websites to avoid incorrect data labels.
Dataset
The dataset collected for this project comprises over 2,100 hand-picked and hand-labeled images spanning more than 60 component manufacturers. Although still growing, the dataset includes companies like Motorola, Acer, Sanyo, Texas Instruments, and many more. Images were collected from mass sales websites like Ebay, Alibaba, and IndianMart, as well as images websites like Google Images, Wikimedia, and TheRetroWeb.
Still unders construction, the model requires each dataset to contain at least 20 images to be considered in its training set. As of writing, the largest manufacturer dataset contains 65 images (Toshiba), while the smallest manufacturer dataset are only 1 image (RCWL).
The bar chart below plots the number of images for each manufacturer as of the time of writing (1/20/2026). The value on top of each bar gives the number of images for a specific manufacturer, and the name of the respective manufacturer is provided below each bar. The red dashed line illustrates the minimum 20-image cutoff. For instance, Fujitsu, Acer, Trident, and 3DFX each contain 20 images, but “ir” (International Rectifier) contains 10 images, while Harris currently contains only six images.
Transfer Learning Model
The model used for this study compares a variety of transfer learning models, including ResNet, EfficientNet, DenseNet, and others. For each model, the base layers remain frozen while the later classifications become flexible to accomodate the 60+ classes of data manufacturers.
Results
Once the data are split, each training set is run through an array of different models, including ResNet, EfficientNet, MobileNet, and DenseNet. Inference is then run on each trained model to find Test Accuracy and the Test F1-score. Results are illustrated in the table below.
Of the models utilized, EfficientNetB7 produced the best results with a test accuracy of 75.91%. Given that the model is being asked to classify between 65 unique manufacturers, this is well above random guessing (1.5% probability of a correct guess. Beyond the model’s performance, it is worth asking how confident the model is when making its decisions — this is illustrated in the plots below.
The plot illustrates the confidence score of the model when it correctly guesses a manufacturer (green bars) versus incorrect guesses (red bars). With a few exceptions, many of the correct guesses come with a high confidence score, typically greater than 80%. When guesses are incorrect, the confidence is very low (~20%).
Clearly, the model is picking up on image details that lead it to make correct predictions. But what details is it picking up on?
In the beginning of our study, we hypothesized that the model would pick up on the manufacturer’s logo — unique shapes would give rise to geometric features the model would learn. But by looking at attention heatmaps from successful predictions (below), we find this is not the case.
The above image is a composite image showing the original component image overlayed with a semi-transparent heatmap. Locations where the heatmap is red indicate a greater focus of attention in the image by the computer vision model; blue and green portions indicate very low attention. Inscribed on top of these are lime green rectangles with arrows pointing to them. The rectangles were added after the attention maps were generated to clearly illustrate the location of the logo we hypothesized the model would pick up on.
Looking at the rectangles, though, we see this is clearly not the case. In each example, the Qualcomm, Geode, Vishay, Intel, and Motorola logos fall well outside the central point of the model’s attention! What, then, is it looking at?
In general, the model seems to have found a shortcut, and has begun picking out patterns in the stucture of the model numbers themselves, and not the company logo. Keep in mind that each image in each dataset is explicitly chosen to be different in some way. For example, each chosen component within a manufacturer dataset contains a different part number, and is likely taken with a different background, from a different angle, with different lighting. It is interesting, then, that these are the details it picks up on to achieve its 76% test accuracy! In the case of the SOIC (final image on the right), it seems to almost be identifying the shape and number of nodes connecting the component to the circuit board.
Conclusion
The project to predict integrated circuit component manufacturer is interesting because there are so many details the model can pick up on. While still in flux, the model already demonstrates a high degree of potential, with nearly 80% test accuracy across 65 unique manufacturers.
Once complete, the model can be shared, uploaded, and utilized as part of an automated inspection pipeline. But what for? There are many reasons, including the ability to quickly look up the manufacturer of an outdated part for quick replacement. Amplifying beyond this, however, the model has the ability to grow and support other computer vision models in the same regime, such as damage identification and package prediction models