$GTCH Microchip giant Intel (NasdaqGS:INTC) recently invested $20 billion in a chip-factory project in Ohio. Regarding the CHIPS Act, William Moss, a spokesman for Intel, said in an emailed statement to the Washington Post:
"...the scope and pace of our expansion in Ohio will depend heavily on funding from the CHIPS Act. Unfortunately, CHIPS Act funding has moved more slowly than we expected and we still don't know when it will get done. It is time for Congress to act so we can move forward at the speed and scale we have long envisioned for Ohio and our other projects."
A recent report published by Transparency Market Research found that the global market for SoCs (System-on-a-chip) is projected to grow from roughly $151.8 billion USD in 2021 to $317.8 billion by the end of 2031, with a compound annual growth rate (CAGR) of 8.1 percent in the forecast period 2022-31. Driving the demand for chips, according to the report, is the "increasing demand for high performance and low cost portable electronic devices and wearable devices such as smartphones, laptops, smartwatches, headsets, etc...," as well as their utility in Advanced Driver Assistance Systems (ADAS).
GBT Technologies Inc. (OTC PINK:GTCH), an early stage technology developer in (IoT), and Artificial Intelligence (AI) Enabled Mobile Technology Platforms, is an example of a firm innovating in the microchip space, with its 3D, multiplanar, integrated circuits (IC) design and manufacturing technology. Most recently, GBT announced that it is researching the development of a machine learning-driven, automated integrated circuits design environment, enabling Fast-Track, Design-to-Silicon capabilities. Regarding the development, the Company's CTO, Danny Rittman explained:
"We are researching an IC solution that would target small start-up companies to large corporations in the semiconductor industry with varying design requirements. With GBT's new approach, we aim to develop a machine learning-driven, one automated IC design flow, enabling Fast-Track Design-to-Silicon for IC design houses. The way we want to do this is by combining traditionally separate front-end and back-end chip design flows into one integrated environment that accelerates the overall design cycle and reducing the IC development costs. A typical microchip design process includes many steps which are classified as front-end and back-end tasks. Various steps are executed using separate EDA tools which require vast amount of integration and design environment adjustments. The new, machine learning-driven flow that we are researching aims to provide one-stop design environment advanced capabilities, with high levels of automation, with the goal of enabling the delivery of superior quality designs, with much faster completion time. The usage of our deep learning and advanced computational geometry algorithms aims to produce a comprehensive design environment, enabling efficient digital/analog design and implementation, particularly with advanced manufacturing nodes. In addition, we are also researching incorporating other capabilities into the system which may include functional and physical verification, simulations, power optimization, characterization, or yield management. With this research we aim to standardize digital and analog IC's design, simulation, verification and characterization. We firmly believe that a one, intelligent, automated IC design environment will introduce a significant productivity enhancement for IC design firms, reducing their overall projects design time and bringing them faster to market."
