Electronic development automation

The complex electronic development is therefore often very time-consuming and error-prone what turns into the bottleneck of every development project. The existing slow development processes could not keep up with the rapidly growing demand for electronics, which is driven by digitalization and trends such as (industrial) IoT. In addition, the number of electronics graduates from universities is declining and is already causing a significant shortage of urgently needed skilled workers.

A B2B SaaS platform offers a holistic solution for electronics development in which the automatable development steps are actually automated and radically accelerated. The user describes the functionality and requirements of the PCB in a flowchart and then receives the component list, schematics and PCB layout at the push of a button, which are developed in a very short time with the help of a complex algorithm and AI.

The provider takes a very forward-looking approach to the projects. This means that the customer knows about all features, but can also make requests for new features. Missing general features can be a roadblock, but the provider stays in contact with potential customers and informs them about the implementation of these features. Very individual features are project-related and can be part of the start-up phase. 

Standardisierung durch eine modulare Elektronikentwicklung, so dass der Entwickler Elektronikmodule wiederverwenden kann, jedoch ohne die Unterstützung von Algorithmen und KI. Dies führte unausweichlich zu einer hohen Fehleranfälligkeit und geringem IP-Austausch innerhalb der Firma. 

Implementation of the database structures (0.5 FTE for 3 months) and optional training of staff (approx. 1 day) to use the software as effectively as possible. 

Especially in the early phase of development projects to achieve fast first results in order to be able to estimate costs and start software development at an early stage.

• The time required is considerably reduced, especially in the early phase of a development. Without exception, a reduction of the development time by more than 90% was achieved.
• The time required for subsequent sample stands is also reduced and delivers a result that is factors faster than the conventional method. Here the algorithm has to calculate a little longer because the requirements for form factor and packing density usually increase. Therefore one can assume calculation times of 1-7 hours, but the algorithm also likes to calculate at night or on weekends.
• The customer achieves the ROI within the first projects.
• The software can be used from day one.
• The short computing times, especially at the beginning, allow a quick estimation about the costs of the PCB, the start of the software development or tests of the hardware. Form factor and packing density are of less importance for this. 

• Evaluation project. The customer provides a small database to compare a past project of the customer with the effort and the result of Celus' software.
• Starter package. The customer prepares the data for the database and the provider provides the interface to the algorithm in the cloud.
• Subscription or license. The customer can use the software and expand the database on his own. Updates for the software are provided continuously by the provider. 

Electronics experts are in demand for the maintenance of data, the creation and import of new modules and the further processing of the generated PCBs. Using the platform itself has only one skill requirement: knowing what the requirements and functions of the PCB are. Software developers or other technicians, for example, can use this platform to generate initial designs. Building the customer's own database takes about half a FTE for 3 months.

•  Customer makes his customer database available "on premise" and prepares it
• An encrypted connection between customer and algorithm in the cloud is established.
• Transfer to a SaaS model, with ongoing updates from Celus and database expansion from the customer side.

Technical facts:

The solution is market-ready and can already be used by a large number of customers. AI, machine learning and reinforcement learning are used. 

APIs/Integrations:

The results are compatible with various CAD tools (Autodesk Eagle, Mentor Graphics PADS Professional, Zuken Cadstar and soon Altium Designer and other tools). There the user can further edit the generation results if required. 

Initial cost estimates can also be made by connecting the APIs of online parts markets. 

Where is the data located?

The customer has the choice between a database in a cloud controlled by the provider in Germany or an "on-premise" database. 

The algorithm and AI is located in the cloud to ensure continuous updates. In the "on-premise" case, the data is transferred encrypted to the cloud and not stored there and the generated files are sent encrypted back to the customer.