Humans have continuously invented sophisticated tools to make life easier, and one of the most impactful inventions is computing devices like smartphones and computers. These devices have become an integral part of our daily lives, helping us with tasks ranging from waking up to an alarm, tracking our daily activities, finding a place to eat, and even monitoring our sleep. However, this transformation did not happen overnight but is the result of centuries of human effort, starting from early counting on fingers to the development of complex computing systems. Today, it is almost impossible to imagine life without these computational tools. With advancements in technology, particularly artificial intelligence (AI), the demand for processing vast amounts of data and information is continuously growing, driving the need for even more powerful and efficient computing solutions. Modern-day computers are digital in nature, meaning they process information in the form of binary digits (0s and 1s). In contrast, early mechanical and electronic computers were analog systems. Analog computers are fast and use continuous signals such as electrical voltages, mechanical movements, or fluid pressure to perform calculations. They excel in solving differential equations, real-time simulations, and signal processing tasks. However, they have limitations such as lower precision, susceptibility to noise, and difficulty in programmability, making them suitable only for specific tasks like weather modelling, control systems, and early scientific simulations. Additionally, analog computing devices are large and have limited scalability due to their reliance on physical components such as resistors, capacitors, and mechanical parts, which become difficult to miniaturize and integrate. On the other hand, digital computers are highly accurate, versatile, and capable of performing complex computations. The miniaturization of transistors has made digital electronic computers more compact and significantly faster.Hybrid computing, which combines both analog and digital systems, improves computational efficiency. In the past, hybrid computers were used to speed up and enhance computation for specific tasks. Analog components processed continuous signals quickly, while digital components provided accuracy and handled complex calculations. For example, in signal processing, hybrid systems were used for real-time tasks like radio detection and ranging (Radar), sound navigation and ranging (Sonar), and weather forecasting, where analog parts quickly processed signals and digital parts analysed and refined the data. In scientific simulations, hybrid computers solved complex problems like fluid dynamics and nuclear reactor simulations faster by using analog components for continuous calculations and digital systems for precision and control. In addition to digital and analog electronic computers, several other computational platforms are being explored, including neuromorphic computing, optical computing, quantum computing, and biomolecular or Deoxyribonucleic acid (DNA) computing. Due to its massive parallelism and high data storage density, DNA computing shows exceptional potential for solving complex mathematical problems, such as the Hamiltonian path problem. However, it faces significant challenges in performing simpler calculations, such as finding the square root of a number. Building on this, we propose a novel biohybrid computation platform and present a proof-of-concept that integrates computation in liquid with traditional digital electronics to exploit potential of both computational systems. This platform combines microfluidics, Ta2O5/Si ion-sensitive field-effect transistors (ISFETs), and an analog (bio/chemical)-digital readout system capable of performing NOT, AND, and OR logic operations, referred to as Electro(Bio)chemical Logic Gates (ELGs), using pH as a chemical information, with potential extension to biological information such as DNA. Considering the need for beyond-silicon materials for both scalability in solid-state transistors and enhanced biomolecular sensitivity in liquid-gated field-effect transistors (LG-FETs), we explored two dimensional molybdenum disulfide (MoS2) for LG-FETs. Firstly we optimized the transfer of metal organic chemical vapor deposition (MOCDV) grown MoS2 on sapphire substrate and studied the impact of potassium hydroxide (KOH) during PMMA-assisted wet transfer process. KOH concentration in the range of 2-5M was found to be suitable for fast transfer with minimal affect on MoS2 quality. To study the impact of different dielectric interfaces, MoS2 LG-FETs were fabricated on 1.5 x 1 cm2 silicon oxide (SiOx), aluminium oxide (AlOx), hafnium oxide (HfOx) and magnesium fluoride (MgFx) dielectric substrates using standard lithography process. Devices were measured using liquid-gate configuration in phosphate buffer saline (PBS) solution with silver/silver chloride (Ag/AgCl) reference electrode as gate. MoS2/MgFx LG-FETs showed lowest subthreshold swing (SS) values (around 85 mV/dec), while MoS2/SiOx LG-FETs exhibited highest field-effect mobility (around 17 cm2.V-1.s-1). MoS2/HfOx LG-FETs showed highest hysteresis (around 100 mV), while other LG-FETs showed small hysteresis (around 25 mV). LG-FETs with different substrate dielectric interfaces showed good stability in PBS solution tested up to 1hours. For a stable biomolecular recognition platform, covalent functionalization is essential, but this is a challenging task on the pristine MoS2 surface. To address this, a dielectric layer is deposited to both functionalize the surface and improve the stability of the LG-FETs. We employed a novel Ion Beam Sputtering (IBS) technique to deposit an AlOx dielectric layer on MoS2. MoS2 LG-FETs showed an enhancement in the stability in PBS solution by reducing device drift, without increasing hysteresis values. The integration of MoS2 LG-FETs with ELGs could serve as the foundation for highly sensitive information transfer between biological and electronic systems, enabling the execution of complex operations within this biohybrid computational platform.
Animesh Pratap Singh (Wed,) studied this question.
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