طراحی یک تقویتکننده عملیاتی دو طبقه با استفاده از شبکه عصبی مصنوعی

محورهای موضوعی : انرژی های تجدیدپذیر

علیرضا پورخلیلی ¹ (دانشکده مهندسی برق- واحد نجف‌‌آباد، دانشگاه آزاد اسلامی، نجف‌‌آباد، ایران)
سید محمد علی زنجانی ² (مرکز تحقیقات ریزشبکه‌‌های هوشمند- واحد نجف‌‌آباد، دانشگاه آزاد اسلامی، نجف‌‌آباد، ایران)

تاریخ دریافت : 1401/04/10 تاریخ پذیرش : 1401/08/09 تاریخ انتشار : 1403/06/01

کلید واژه: شبکه‌ عصبی مصنوعی, مصالحه, خودکارسازی طراحی الکترونیکی, بهینهسازی طراحی آنالوگ, تقویت‌‌کننده عملیاتی, مدل‌‌کردن عملکرد,

چکیده مقاله :

طراحی مدارهای مجتمع آنالوگ با پیچیدگی بالا، نیازمند انتخاب مناسب پارامترهای مختلف طراحی مثل نسبت عرض به طول کانال، مقدار خازن جبران و خازن بار است، به‌‌نحوی که در اثر این تغییرات، پارامترهای مطلوب کاربران مانند بهره‌‌، پهنای باند، توان مصرفی و حاشیه فاز، بهبود یابد. با توجه به کارهای انجام شده در این زمینه، در این مقاله یک تقویت‌‌کننده عملیاتی دو طبقه با زوج ورودی پی موس (PMOS) و جبران‌‌ساز میلر، به‌‌کمک یک شبکه‌ عصبی مصنوعی طراحی شده است. داده‌‌های ورودی شبکه‌ عصبی، چهار پارامتر عملکرد مداری یعنی بهره فرکانس پایین، پهنای باند، توان مصرفی و حاشیه‌‌ فاز است و در خروجی، مقدار عرض و طول کانال ترانزیستورها، منبع جریان مرجع، خازن جبران و خازن بار حاصل می‌‌شود. در این طراحی، از روش نمونه برداری مبتنی‌بر شبیه‌‌سازی‌‌های موازی اچ-اسپایس برای گردآوری داده از فضای 15 بعدی طراحی استفاده شده است که منجر به سادگی و خودکارسازی فرایند تهیه مجموعه داده‌‌های آموزشی و کاهش زمان نمونه برداری شده است و سپس این داده‌‌ها برای آموزش مدل عصبی استفاده شده‌‌اند. در مرحله‌‌ بعد، از روش نمونه‌‌برداری بازه‌‌ای برای ایجاد طراحی‌‌های جدیدی از مدل عصبی آموزش‌‌دیده، بهره گرفته شده که باعث سهولت فرایند طراحی شده است و امکان انجام انواع مصالحه مورد نظر کاربر بین پارامترهای عملکرد مختلف تقویت‌‌کننده را فراهم کرده است. همچنین اگر ضریب شایستگی (FOM) از تقسیم حاصل‌‌ضرب پهنای باند واحد در خازن بار به توان مصرفی به دست آید، مقایسه طراحی‌‌های حاصل شده از روش ارائه شده در این مقاله، با برخی از روش‌‌های به کار رفته برای طراحی تقویت‌‌کننده‌‌ها‌‌ی عملیاتی با ساختار مشابه در مطالعات قبلی، نشان می‌‌دهد که این پارامتر، حداقل 154 درصد افزایش یافته است.

چکیده انگلیسی:

Design of complex analog integrated circuits requires the appropriate choice of various design parameters such as MOSFET’s aspect ratio, compensation capacitance and load capacitance in a way that improves user’s desired parameters like gain, bandwidth, power dissipation and phase margin. Considering previous works, in this paper, a two-stage miller compensated operational amplifier with PMOS input pair is designed using artificial neural network. The inputs of the neural network are design parameters including DC gain, bandwidth, power dissipation and phase margin and in its output, the sizing of transistors and the amounts of reference current supply, compensation capacitance and load capacitance are acquired. In this design method, a sampling method based on parallel HSPICE simulations is employed for data acquisition from the 15-dimensional design space which results in simplicity and automation of the dataset collecting procedure and reduces the total sampling time and then this data is used for training the neural network model. In the next stage, a range sampling method is applied for making new designs from the trained model which has facilitated the design procedure and made the user-desired tradeoffs between different performance parameters of the operational amplifier possible. Moreover, if the amplifier performance figure of merit (FOM) is defined as the result of the multiplication of unity gain bandwidth and load capacitance divided by power consumption, the comparison between obtained designs of this paper’s proposed method and the results of some other methods applied for designing operational amplifiers with relatively similar topologies in previous works, indicates that this parameter has increased by 154% at the minimum.

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